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Record W4362470524 · doi:10.1558/rosa.25456

Getting into the mind of Medhatithi

2023· article· en· W4362470524 on OpenAlexaff
Elisa Freschi

Bibliographic record

VenueReligions of South Asia · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConsistency (knowledge bases)Corporal punishmentPunishment (psychology)Selection (genetic algorithm)EpistemologyCulminationPsychologyProcess (computing)Social psychologySociologyComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper analyses Medhatithi’s discussion of corporal punishment, with special reference to the debate staged in his commentary on MDh 8.318. His arguments are extremely sophisticated, especially because of the application of Mimamsa-influenced reasoning rules. The paper makes implicit steps and unspoken hypotheses explicit and highlights the selection process through which Medhatithi finally selects one solution to the controversy he examines over the others. For some instances of possible candidates: Is analogical reasoning able to provide stronger support than, for example, authoritative statements? What role does inner consistency play? Which criterion wins in case of conflicts among different textual passages? To test the inner-consistency criterion, the paper tackles the issue of corporal punishment as discussed in different contexts and tries to solve the seeming clashes that arise when different texts by Medhatithi are juxtaposed. It concludes by seeing Medhatithi’s commentary on MDh 8.318 as the culmination of a systematisation attempt regarding all cases of corporal punishment as distinctly ordained based on the purpose to be achieved.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.023
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.239
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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