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Record W4410766104 · doi:10.31235/osf.io/kqvea_v1

The Disciplinary Value of Linguistic Capital in the Social Sciences and Humanities

2025· preprint· en· W4410766104 on OpenAlexaboutno aff
Julien Larregue, Alice Pavie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsDisciplineValue (mathematics)Digital humanitiesSociologyLinguisticsHumanitiesSocial capitalSocial sciencePhilosophyMathematics

Abstract

fetched live from OpenAlex

This article examines the relationship between linguistic practices and funding success in Canadian social sciences and humanities. Through a mixed-methods approach combining data on 56,680 successful and unsuccessful grant applications submitted to the Canadian Social Sciences and Humanities Research Council and 45 interviews with past members of review committees, we analyse how language intersects with knowledge hierarchies and disciplinary cultures. Our findings show that writing a grant proposal in English rather than in French is associated with slightly higher chances of securing funding, mostly reflecting the greater recognition of applicants who have published in prestigious anglophone journals. However, the worth of this linguistic capital varies significantly across disciplines. These differences stem from how each discipline defines scientific value—whether through a more universal or context-dependent perspective and according to singular or plural hierarchies.

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)0.027
metaresearch head score (Gemma)0.113
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.524

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.113
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.009
Science and technology studies0.0080.021
Scholarly communication0.0100.003
Open science0.0010.013
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.320
Teacher spread0.262 · 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
Published2025
Admission routes1
Has abstractyes

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Same topicSecond Language Learning and TeachingFrench-language works237,207