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Record W4226336313 · doi:10.1093/jhs/hiac003

Pāli Studies in Colonial Bengal: Bengali-Speaking Buddhists’ Strategy to Distinguish themselves from Hindus

2022· article· en· W4226336313 on OpenAlexaff
D. Mitra Barua

Bibliographic record

VenueThe Journal of Hindu Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsYork University
Fundersnot available
KeywordsBengaliBENGALBuddhismSanskritColonialismHinduismScholarshipEnthusiasmCeylonIndigenousReligious studiesPolitical scienceGender studiesHistoryAncient historySociologyTheologyPhilosophyLiteratureLawArt

Abstract

fetched live from OpenAlex

Abstract This article focuses on one of the core strategies used to decouple Buddhism from Hinduism in colonial Bengal. Capitalising on the nineteenth-century enthusiasm in Indo-European languages and public education, marginalised Buddhist minority argued that what Sanskrit meant for Hindus was Pāli for Buddhists to secure grant-in-aid for Pāli studies. With the assistance from Buddhists from Ceylon, they introduced Pāli weekly classes at village temples and primary, secondary, and postsecondary schools with Buddhist students in Chittagong. They convinced the colonial government to fund Pāli Departments at Chittagong College and at the University of Calcutta and more importantly to establish a ‘state scholarship for the scientific study of Pāli in Europe’ in 1915 that produced arguably the first indigenous Buddhologist. I contend that Pāli studies not only gave Bengali-speaking Buddhists access to modern education, but also enabled them to distinguish themselves from Hindus and emerge as a distinct religious community.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.013
Scholarly communication0.0090.003
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.186
GPT teacher head0.432
Teacher spread0.247 · 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 designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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