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Record W4200130592 · doi:10.1002/jid.3605

Working while studying: Employment premium or penalty for youth?

2021· article· en· W4200130592 on OpenAlexaff
Sènakpon Fidèle Ange Dedehouanou, Luca Tiberti, Gbodja Hilaire Houeninvo, Djohodo Inès Monwanou

Bibliographic record

VenueJournal of International Development · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWork (physics)Sample (material)Demographic economicsSchool-to-work transitionControl (management)Transition (genetics)HazardDeveloping countryPsychologyEconomicsLabour economicsEconomic growthVocational educationEngineeringManagement

Abstract

fetched live from OpenAlex

Abstract Most youths in developing countries leave school with only a general academic education level, slowing down their transition to the labour market. We analyse whether work experience during school can ease youth transition to first job in Benin. Using data from the 2014 School‐to‐Work Transition Survey (SWTS), we estimate a three‐equation model to control for endogenous treatment assignment and sample selection and a hazard frailty model. We find that working while studying eases transition from school to first job. However, these findings were significant only for men and youth who left school with at least a secondary education.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.117
GPT teacher head0.279
Teacher spread0.162 · 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
Published2021
Admission routes1
Has abstractyes

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