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Record W3134932868 · doi:10.1017/dem.2020.21

Those who can't sort, steal: caste, occupational mobility, and rent-seeking in rural India

2021· article· en· W3134932868 on OpenAlex
Nicholas Lawson, Dean Spears

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Demographic Economics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Economic Development in India
Canadian institutionsUniversité du Québec à Montréal
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsCastesortSociologyBusinessPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

Three important features of Indian labor markets enduringly coexist: rent-seeking, occupational immobility, and caste. These facts are puzzling, given theories that predict static, equilibrium social inequality without conflict. Our model explains these facts as an equilibrium outcome. Some people switch caste-associated occupations for an easier source of rents, rather than for productivity. This undermines trust between castes and shuts down occupational mobility, which further encourages rent-seeking due to an inability of workers to sort into occupations. We motivate our contribution with novel stylized facts exploiting a unique survey question on casteism in India, which we show is associated with rent-seeking.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.286
Teacher spread0.259 · 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