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Record W2527113091 · doi:10.5376/jtsr.2016.06.0008

Wage determination machinery of tea industry in India: a case of West Bengal State

2016· article· en· W2527113091 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Tea Science Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsWest bengalWageBENGALState (computer science)BusinessAgricultural economicsEconomicsLabour economicsSocioeconomicsEngineeringMathematicsCivil engineering

Abstract

fetched live from OpenAlex

Daily rate of wages for tea plantation workers in west Bengal is abysmally low in comparison to agricultural minimum wages. There are a few benefits offered by the employers apart from the statutory benefits enshrined in the Plantation Labour Act, 1951. Consequently, many tea workers in this state are suffering from starvation, malnutrition, debt, etc. In this backdrop, in my present study, a deliberate attempt has been made in order to highlight the actual picture of wage and its determination process in the tea industry located in West Bengal in India.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.076
GPT teacher head0.352
Teacher spread0.276 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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