MétaCan
Menu
Back to cohort
Record W2943845719 · doi:10.1596/1813-9450-8851

The Apprenticeship-to-Work Transition: Experimental Evidence from Ghana

2019· book· en· W2943845719 on OpenAlexaff
Morgan Hardy, Isaac Mbiti, Jamie McCasland, Isabelle Salcher

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2019
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsApprenticeshipWork (physics)Transition (genetics)School-to-work transitionSociologyEngineeringGeographyPedagogyMechanical engineeringChemistryVocational educationArchaeology

Abstract

fetched live from OpenAlex

This paper examines the effects of a government-sponsored apprenticeship training program designed to address high levels of youth unemployment in Ghana. The study exploits randomized access to the program to examine the short-run effects of apprenticeship training on labor market outcomes. The results show that apprenticeships shift youth out of wage work and into self-employment. However, the loss of wage income is not offset by increases in self-employment profits in the short run. In addition, the study uses the randomized match between apprentices and training providers to examine the causal effect of characteristics of trainers on outcomes for apprentices. Participants who trained with the most experienced trainers or the most profitable ones had higher earnings. These increases more than offset the program's negative treatment effect on earnings. This suggests that training programs can be made more effective through better recruitment of trainers.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.035
GPT teacher head0.237
Teacher spread0.203 · 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 designNon-randomized trial
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

Citations23
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueWorld Bank, Washington, DC eBooksSame topicLabor market dynamics and wage inequalityFrench-language works237,207