MétaCan
Menu
Back to cohort
Record W2594236544 · doi:10.1111/dpr.12220

The Mahatma Gandhi National Rural Employment Guarantee Scheme: A Policy Solution to Rural Poverty in India?

2017· article· en· W2594236544 on OpenAlex

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

VenueDevelopment Policy Review · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPovertyTamilGovernment (linguistics)Rural povertyWork (physics)Economic growthRural areaBusinessEconomicsPolitical science

Abstract

fetched live from OpenAlex

The Mahatma Gandhi National Rural Employment Guarantee Act ( MGNREGA ) was developed by the Indian government to reduce rural poverty through 100 days of guaranteed employment per year. Using focus group methods, we explore whether this scheme has provided rights' based social protection through guaranteed employment for Scheduled Castes, Scheduled Tribes and women in Kerala, Tamil Nadu and Odisha. We found that the experiences of participating in MGNREGA varied depending on how MGNREGA wages compared to market wages in the region, as well as local implementation of the program. Although MGNREGA offered some basic employment for marginalized groups, it did not provide substantial help to the most vulnerable. However, there was some evidence of small but significant shifts in labour relations. Higher wages, more opportunities for work, better implementation and a greater recognition of the caregiving responsibilities of women will be required for this policy to fully meet its goals.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.308
Teacher spread0.284 · 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