# Part 2 of 4 -- r3-b1 (NAMAMI/India), items 13-24 of 24

Part of the Popper Engine adversarial-rivals Grok pack. See `GROK_PACK_20260806.md` for full operator instructions. Paste each numbered block below as ONE Grok chat turn (a fresh block = a fresh, unrelated turn -- do not chain them in the same conversation, so one item's evidence never leaks into another's answer). Save each reply as raw text into the drop folder named at the end of that block.


################################################################
CONJECTURE r3-b1-21  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** A routed South Asian religious/textual-tradition umbrella (covering Sanskrit/Prakrit/Pali-family material -- not the Sanskrit language alone) accounts for more than 20% of records in every complete state.

**Proposed mechanism:** The floor is set just below the lowest training observation specifically because that training state has an unusually large non-South-Asian (Tibetan-tradition) institutional presence; the claim predicts this umbrella tradition remains a substantial presence everywhere even where it is not dominant.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Shares range from 22.42% (the low outlier, the Tibetan-presence state) up to 99.26%. A second state sits at 41.10% for an unrelated reason (a high share of records that resisted any tradition classification at all, not another tradition being dominant).

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; every state's share stayed above the 20% floor.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-21
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-21.md
################################################################

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CONJECTURE r3-b1-23  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** Routing abstention (records that could not be assigned to any tradition at all) never exceeds 45% of records in any complete state.

**Proposed mechanism:** A ceiling on how much of a state's data can resist tradition-routing at all, given the routing instrument's current classification logic. Training already spans a very wide range, so the ceiling was set well above the observed maximum rather than tightly.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Abstention shares range from 0.13% up to 41.26% (an extreme outlier, roughly 3.8x the next-highest training state at 10.86%).

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; every state's abstention share stayed under the 45% ceiling.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-23
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-23.md
################################################################

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CONJECTURE r3-b1-24  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** Within the tradition-routing instrument's own accounting, one specific failure mode -- a real language/script value present in the source data but not yet recognized by the routing instrument's own vocabulary snapshot (a curation gap, distinct from a genuinely blank field) -- never accounts for 35% or more of a complete state's records.

**Proposed mechanism:** Distinguishes a data-CURATION gap (fixable by extending the routing instrument's own vocabulary, a project-side limitation) from a genuine source-data gap (blank fields in the original catalogue, a different claim).

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** One state shows this failure mode on 29.45% of its records (the lone large outlier); every other complete state is under 1.4%.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs, including particular interest in whether this pattern was a one-off or would recur in another large state; every tested state's share stayed under the 35% ceiling.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-24
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-24.md
################################################################

################################################################
CONJECTURE r3-b1-25  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The genuinely-blank-field failure mode (both the language and script portal fields are blank or a null-token for a record) never accounts for 15% or more of a complete state's records.

**Proposed mechanism:** The straightforward source-data-completeness ceiling counterpart to the curation-gap ceiling described above, covering records where the underlying catalogue entry itself simply lacks language/script information.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** This failure mode's share ranges up to 11.80% (the training maximum) in one state; two more states sit above 10%, most others are under 3.6%.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; every state's share stayed under the 15% ceiling.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-25
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-25.md
################################################################

################################################################
CONJECTURE r3-b1-26  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** A third routing-instrument failure mode -- the language field is blank/unusable but the script field alone was enough to identify the tradition -- never accounts for 2% or more of a complete state's records.

**Proposed mechanism:** Proposed to reflect one specific state's institutional cataloguing pattern (Tibetan-script rows there most often carry a usable script token even when the language cell does not parse cleanly), making that state the natural training-side maximum for this specific pathway.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Maximum 1.60% (the Tibetan-presence state); every other complete state under 0.84%.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; every state's share stayed under the 2% ceiling.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-26
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-26.md
################################################################

################################################################
CONJECTURE r3-b1-27  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** A fourth, rarest routing-instrument pathway -- a non-South-Asian script (Southeast Asian, Sinitic, Inner Asian, Tibetan, or Zoroastrian) overriding an otherwise South-Asian-facet language token -- never accounts for 0.1% or more of a complete state's records.

**Proposed mechanism:** Included for systematic completeness across the routing instrument's full set of decision pathways, rather than because this specific pathway was expected to be a risky or informative test; it is by far the lowest-volume of the six pathways in the training data.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Maximum 0.033% (107 records, in the Tibetan-presence state); every other complete state lower, several at exactly 0.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; every state's share stayed under the 0.1% ceiling.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-27
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-27.md
################################################################

################################################################
CONJECTURE r3-b1-31  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** A routed 'sanskritic' language grouping (pure Sanskrit/Prakrit/Pali-family tokens) outnumbers a routed 'indo_aryan' grouping (vernacular Hindi/Bengali/Gujarati/etc. tokens) in every complete state except one, where the vernacular tradition dominates instead.

**Proposed mechanism:** The one training-side exception is attributed to a documented regional vernacular-manuscript tradition (in an eastern state with a strong vernacular-language literary culture). The claim explicitly flagged, in advance, one specific holdout state as a plausible SECOND exception, on the grounds that it has its own well-documented strong vernacular/community manuscript tradition -- a mechanism structurally similar to the training-side exception.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Sanskritic-to-vernacular ratios range from 0.45 (the sole training exception, vernacular-dominant) up through several states in the 1.1-5.5 range to over 5,000 (essentially all-sanskritic) in the least-vernacular state. The closest non-exception state to flipping sits at a ratio of 1.24.

**Holdout test and outcome:** Two further vernacular-dominant states emerged in the holdout: one at a ratio of 0.71 (a large eastern-frontier state whose vernacular language patterns with the training-side exception's own vernacular), and one at a sharply vernacular-dominant ratio of 0.05. A third holdout state patterned sanskritic as expected. The specific state flagged in advance as the most likely second exception was not tested at all (it was withdrawn from the batch before the holdout ran). The claim's own registered kill condition names a fixed set of three specific holdout states and requires two of them to flip before it is refuted; with one of those three unavailable, only two remained testable, and not both of those two flipped, so the claim survives on its literal wording. Its substantive content -- that the training-side exception is unique -- is nonetheless now contradicted by two further states outside that named set.

**Status:** Survived on its literal wording; its substantive uniqueness claim is contradicted by two further holdout states.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-31
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-31.md
################################################################

################################################################
CONJECTURE r3-b1-32  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The single most common tradition-routing decision pathway (basis value) accounts for at least 35% of records in every complete state -- i.e. routing decisions are never fragmented across three or more roughly-equal-sized pathways within one state.

**Proposed mechanism:** A floor on how fragmented the routing instrument's basis assignment can get, calibrated tightly (only about 5.6 percentage points above the training-side minimum) because that minimum-training state already shows real fragmentation (several of the instrument's six possible pathways each carrying tens of thousands of records there).

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Minimum 40.62% (the most fragmented training state, on its plurality pathway); every other complete state's plurality pathway carries a much higher share (77% up to 99%).

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; every state's plurality-pathway share stayed above the 35% floor.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-32
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-32.md
################################################################

################################################################
CONJECTURE r3-b1-33  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The fraction of a state's holding-institution NAME labels written in non-Latin script is substantially and cleanly higher in Hindi/Devanagari-administrative-belt states than in non-Hindi-belt states -- and this does not simply track whether the state's own official language is itself written in Devanagari (one training state uses Devanagari for its own official language but shows a near-zero non-Latin institution-name share).

**Proposed mechanism:** Institution-NAME transliteration practice (an entirely different axis from the script of the cataloged manuscripts themselves) is proposed to track the administrative-language convention the cataloguing institution itself operates under, not the script of the material it holds.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it. Grain: holdings-weighted share of distinct institution-name strings containing any non-Latin Unicode character, per state.

**Training-side evidence:** Belt-state minimum 45.03%, cleanly separated (more than 3x) from the non-belt maximum of 14.10%; one belt state reaches 92.01%. A later, independent investigation traced part of one belt state's high figure to records entered by a single out-of-state cataloguing centre working in a different non-Latin script -- confirming the classifier genuinely measures non-Latin SCRIPT specifically (any non-Latin script, not only the belt's own), a real, disclosed, unexcluded rival reading of what drives part of the belt-state signal.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs. One holdout state with substantial holdings in a non-Devanagari, non-Latin script nonetheless catalogued those holdings under Latin-script (romanized) institution names -- a neutral finding showing institution-name script and holdings content are independent axes, not counted against the belt/non-belt claim itself. Another holdout state (carved administratively out of a training-belt state) showed the strongest belt-pattern confirmation in the whole project: 86.17% non-Latin institution names. Remaining holdout states showed shares below the belt minimum, consistent with patterning non-belt.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-33
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-33.md
################################################################

################################################################
CONJECTURE r3-b1-34  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** A scope-integrity flag (marking every record as holdings data, not origin/production data) is set correctly on 100% of records in every complete state -- zero exceptions.

**Proposed mechanism:** Not a substantive claim about the world; verifies that the project's own holdings-vs-origin bookkeeping was applied correctly by the harvest across every record.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Exactly zero exceptions across all ~4.22M complete-state records in all 11 states.

**Holdout test and outcome:** Tested across all nine India holdout states/UTs; zero exceptions found.

**Status:** Survived every tested holdout state (exact-zero integrity check).

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-34
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-34.md
################################################################

################################################################
CONJECTURE r3-b1-35  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The date-precision field equals 'unknown' (never any other value) on 100% of records in every complete state -- a stricter, exact-zero version of a related, more permissive ceiling claim tested elsewhere in the same project on the same underlying field.

**Proposed mechanism:** The same harvest-method fact as the dated-fraction claim above (no date column is parsed by this source's list-page enumeration method), tested here at the strict exact-zero threshold rather than a generous ceiling.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** Exactly zero non-'unknown' records across all ~4.22M complete-state records in all 11 states. A small number of date-precision-populated records observed elsewhere in the full national dataset were traced to two states entirely outside these 11 training states.

**Holdout test and outcome:** Tested (with the same detail-enrichment-pilot exclusion filter described above) across all nine India holdout states/UTs; zero exceptions found.

**Status:** Survived every tested holdout state (exact-zero integrity check).

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-35
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-35.md
################################################################

################################################################
CONJECTURE r3-b1-40  --  PASTE THIS ENTIRE BLOCK AS ONE GROK MESSAGE
################################################################

You are acting as an independent scientific referee. You will be shown ONE
empirical conjecture from a data-driven research project that catalogues
historical manuscripts and inscriptions, together with: the mechanism its
authors proposed to explain it, a summary of the training evidence that
supported it while it was being developed, and the outcome of a single
pre-registered test the authors later ran against held-out data (data that
was not consulted while the conjecture was written).

Your task is not to defend or attack the conjecture's authors. Your task is
to ask a narrower question: is there a DIFFERENT causal mechanism, distinct
from the one proposed, that would produce the same training-evidence pattern
AND the same held-out outcome equally well or better? A mechanism that is
merely "also consistent with the data" is not enough — it should be a
genuine rival explanation, one a careful skeptic would actually worry about.

## The conjecture

**Claim (as registered):** The folio/leaf-count field is populated on fewer than 0.5% of records in every complete state -- a stricter, India-only version of a related cross-source ceiling tested elsewhere in the same project (which compares against a non-India comparator source at a looser 1% threshold).

**Proposed mechanism:** The same harvest-method-completeness fact described above (list-page enumeration does not parse an extent/folio-count field), tested here at a tighter, India-only threshold without reference to any external comparator.

**Scope:** Source: NAMAMI (India), a state-facetted manuscript-holdings portal (holdings, not origin -- every record is held-by, not produced-by, the state). Training: 11 states with a complete portal walk (~4.22M records: Bihar, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Tamil Nadu, Uttar Pradesh, West Bengal). Holdout: 9 more states/union territories walked to completion after this claim was fixed (Jammu & Kashmir, Assam, Haryana, Delhi, Manipur, Puducherry, Punjab, Uttarakhand, Jharkhand), each landed as a complete, reconciled count before testing. All data comes from one portal, so survival across states shows stability within this portal's own cataloguing practice, not independently of it.

**Training-side evidence:** All 11 complete states show EXACTLY 0% populated. The small fraction of records nationwide that DO carry this field (about 0.27% of all NAMAMI records at the time) is 100% concentrated outside these 11 states.

**Holdout test and outcome:** Tested (with the same detail-enrichment-pilot exclusion filter described above) across all nine India holdout states/UTs; stayed under the 0.5% ceiling throughout.

**Status:** Survived every tested holdout state.

## What we need from you

For this conjecture, propose UP TO THREE rival mechanisms. For each rival,
give:

1. **mechanism** — the rival causal mechanism, stated plainly, in one or two
   sentences. It must be genuinely distinct from the stated mechanism above,
   not a rewording of it.
2. **why_it_explains_the_same_evidence** — why this rival would produce the
   same training pattern AND the same held-out outcome described above, at
   least as well as the stated mechanism does.
3. **named_confounds** — the specific confound(s) this rival mechanism turns
   on (for example: cataloguing or data-entry practice, survival or
   preservation bias, a unit-of-count or denominator effect, a harvest or
   digitization artifact, an administrative or geographic classification
   artifact, selection effects in what got catalogued at all). Name the
   actual confound(s) at work here — do not just gesture at "bias" in the
   abstract.
4. **discriminating_test** — a concrete, specific, and in-principle
   computable procedure that would produce a DIFFERENT predicted result
   under your rival mechanism than under the stated mechanism. State: what
   you would measure, and what result would come out differently. Say
   whether it could run against data of the same kind already described
   above (the same catalogue or an extension of it), or would need a
   different dataset.

**Quality over count.** If you cannot construct a genuine rival that is at
least as plausible as the stated mechanism, do not invent a weak one just to
fill the list. Declaring no viable rival is a MORE useful and MORE valued
answer than a strained one — a rival that is obviously worse than the
original actively harms this exercise, because it wastes the reader's time
checking a mechanism no one should have believed in the first place. Zero,
one, two, or three rivals are all acceptable outputs. Most conjectures will
not have three good rivals; some will have none.

## What you are not given, and should not guess at

You are not told how close to its pass/fail boundary this conjecture's
held-out test came, how any other conjecture in the same project fared, or
how this result will be used downstream. Do not speculate about any of
that. Evaluate this conjecture entirely on its own terms, using only the
evidence given above.

## Output format

Respond with STRICT JSON ONLY. No prose before or after the JSON, no
markdown code fences, no commentary outside the JSON structure itself. The
JSON must have exactly this shape:

{
  "rivals": [
    {
      "mechanism": "string",
      "why_it_explains_the_same_evidence": "string",
      "named_confounds": ["string", "..."],
      "discriminating_test": {
        "description": "string",
        "computable_on_held_data": true,
        "what_result_favors_rival": "string"
      }
    }
  ],
  "no_viable_rival": false,
  "notes": "string"
}

Rules for the JSON:
- "rivals" is an array of zero to three objects, in the shape shown above.
  Zero entries is valid and expected whenever no genuine rival exists.
- If "rivals" is empty, set "no_viable_rival" to true. If "rivals" is
  non-empty, set "no_viable_rival" to false.
- "named_confounds" is an array of short strings (one per confound named).
- "computable_on_held_data" is a boolean: true if the discriminating test
  could be run using data of the same kind described above (the same
  catalogue/source, re-queried or extended), false if it would require a
  fundamentally different dataset not described above.
- "notes" is a string for anything else worth recording; use an empty
  string if there is nothing to add. Do not use it to restate the rivals.
- Do not include any keys other than the ones shown above, at any level.
- Do not wrap the JSON in a code fence (no ``` characters). Output the JSON
  object itself and nothing else — your entire response must be valid JSON,
  parseable with no pre- or post-processing.

################################################################
END OF r3-b1-40
Save Grok's reply, verbatim, as plain text, to:
  docs/generated/popper_rivals/r3-b1/grok/r3-b1-40.md
################################################################
