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Record W3103142363 · doi:10.1177/0049085720957512

Access to Non-farm Employment in Contemporary India: A Study of Bihar and Punjab

2020· article· en· W3103142363 on OpenAlexaff
Gurpreet Singh

Bibliographic record

VenueSocial Change · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsCasteLivelihoodAgricultureDiversification (marketing strategy)Agrarian societyContext (archaeology)BusinessEconomic growthSocioeconomicsWork (physics)Agricultural economicsGeographyEconomicsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Rural non-farm diversification in India is taking up new roles amidst increasing agrarian distress. In this context, two issues have been examined in this paper: first, the nature of rural non-farm diversification, and second, the accessibility of households to rural non-farm employment in the states of Bihar and Punjab. The study is predominantly based on unit level data of the latest round of the Situational Assessment Survey of Agricultural Households (NSSO). Findings suggest that while non-farm activities are largely adopted by landless and marginal land households in both states, there are a few lucrative options available which are being accessed by large landholders in Punjab. Overall, caste, gender and education are dominant determinants that work as barriers to the entry for rural households. The findings recommend that institutional reforms along with public policies should be prioritised towards generating sustainable non-farm livelihood options while eliminating multi-dimensional exclusions in rural labour markets considering regional prerequisites.

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 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)0.000
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 score0.196
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.320
GPT teacher head0.358
Teacher spread0.037 · 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 teacher head, 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

Citations2
Published2020
Admission routes1
Has abstractyes

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