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Record W2148761616

A quarter of a century: mobility and stagnation in India's rural labour market

2013· article· en· W2148761616 on OpenAlexaboutno aff
Rajarshi Majumder

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsnot available
Fundersnot available
KeywordsAgrarian societyIndustrialisationSubsistence agricultureAgricultureQuarter (Canadian coin)EconomicsRural areaDevelopment economicsCapital (architecture)Labour economicsPhenomenonMarket economyEconomic growthGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Transformation of the countryside from agrarian subsistence economy to non-farm monetised economy is propagated as a precursor of growth and development and involves shifting of labour from farming to off-farm activities. India has started its journey in this path but has a long way to go. Researchers also question whether the changing pattern of rural labour is a positive phenomenon or a distress one. This paper attempts to examine the complexity of changes in rural labour market in India over a quarter of a century to untangle the dynamics. It is observed that the changes taking place are not always conducive to progress as a large part of it is distress driven. While some social groups are going up the ladder, a large mass of the others are stagnating in same or similar occupations. It appears that agriculture still holds the key to rural development. A three pronged strategy of agricultural progress, human capital formation, and rural industrialisation is necessary for breaking the shackles of continuity and usher in changes that are real rather than apparent.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.165
Teacher spread0.161 · 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 designObservational
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
Published2013
Admission routes1
Has abstractyes

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