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Degrees without Freedom: The Impact of Formal Education on Dalit Young Men in North India

2004· article· en· W2141452991 on OpenAlexaff
Craig Jeffrey, Roger Jeffery, Patricia Jeffery

Bibliographic record

VenueDevelopment and Change · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Economic Development in India
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsCasteDignityCultural capitalSociologyEconomic growthResentmentGender studiesEmpowermentPolitical scienceSocial scienceLawEconomics

Abstract

fetched live from OpenAlex

Abstract This article considers the capacity of formal education to undermine established processes of caste and class reproduction in an area of north India, with particular reference to the views and strategies of educated Dalit young men. It draws on quantitative and qualitative research conducted by the authors in a village in Bijnor district, western Uttar Pradesh (UP). We discuss how educated Dalit young men perceive education, how they seek to use educational credentials to obtain ‘respectable’ jobs, and how they react when this strategy fails. Increased formal education has given Dalit young men a sense of dignity and confidence at the village level. However, these men are increasingly unable to convert this ‘cultural capital’ into secure employment. This has created a reproductive crisis which is manifest in an emerging culture of masculine Dalit resentment. In response to this culture, Dalit parents are beginning to withdraw from investing money in young mens’ higher secondary and tertiary‐level education. Without a substantial redistribution in material assets within society, development initiatives focused on formal education are likely to be only partially successful in raising the social standing and economic position of subordinate groups.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.092
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.007
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.002
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 head0.051
GPT teacher head0.303
Teacher spread0.252 · 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

Citations129
Published2004
Admission routes1
Has abstractyes

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