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Female labor force participation in the Middle East and North African Countries: constraints and levers

2020· article· en· W4252839132 on OpenAlexaff
Chokri Kooli, Hend Al Muftah

Bibliographic record

VenueBrazilian Journal of Policy and Development · 2020
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGeopoliticsMiddle EastEthnic groupPoliticsDevelopment economicsEconomicsPolitical scienceDemographic economics

Abstract

fetched live from OpenAlex


 
 
 Despite the importance of the subject, few researches have attempted to explain the female labor force participation (FLFP) in MENA region. This research tries to exploring the geopolitical, social, ethnic and economic levers or constraints that facilitate or bloc women’s access to the labor market. A quantitative approach based on data analysis was performed through the review of the World Bank statistics related to women participation in the labor market in MENA region. Data analysis revealed that the studied labor market was marked by an overall positive progress of the presence of female workers. However, this evolution is still considered very low and slow in comparison with world standards. The research also revealed huge disparities in the level of progress of the FLFP among the studied countries. The disparities observed were interpreted in correlation with the religious, economic, political, social and cultural aspects.
 
 

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)0.002
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.057
GPT teacher head0.252
Teacher spread0.194 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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