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Record W2341223996 · doi:10.1093/acprof

The Egyptian Labor Market in an Era of Revolution

2015· preprint· en· W2341223996 on OpenAlexaboutno aff
Ragui Assaad, Caroline Krafft

Bibliographic record

VenueRePEc: Research Papers in Economics · 2015
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentEconomicsLabour economicsSecondary labor marketInequalityLabor relationsEconomic growth

Abstract

fetched live from OpenAlex

This book fills an important gap in the knowledge about labor market conditions in Egypt in the aftermath of the Arab Spring uprisings, and it analyzes the results of the latest round of the Egypt Labor Market Panel Survey carried out in early 2012. The chapters cover topics that are essential to understanding the conditions leading to the Egyptian revolution of 25 January 2011, including the persistence of high youth unemployment, labor market segmentation and rigidity, growing informality, and the declining role of the state as an employer. It includes the first research on the impact of the revolution and the ensuing economic crisis on the labor market, including issues such as changes in earnings, increased insecurity of employment, declining female labor force participation, and the stagnation of micro and small enterprise growth. Comparisons are made to labor market conditions prior to the revolution using previous rounds of the survey fielded in 1988, 1998, and 2006. The chapters make use of this unique longitudinal data to provide a fresh analysis of the Egyptian labor market after the Arab Spring, an analysis that was simply not feasible with previously existing data. This book is essential reading for anyone interested in the economics of the Middle East and the political economy of the Arab Spring. Contributors to this volume - Mona Amer, Cairo University Ragui Assaad, University of Minnesota and the Economic Research Forum Ghada Barsoum, American University in Cairo Asmaa Elbadawy, Independent Consultant Rana Hendy, Economic Research Forum Samer Kherfi, American University of Sharjah Caroline Krafft, University of Minnesota Ali Rashed, Population Council Rania Roushdy, Population Council Mona Said, American University in Cairo Rania Salem, University of Toronto Irene Selwaness, Cairo University and the Population Council Maia Sieverding, University of California, San Francisco Jackline Wahba, University of Southampton Chaimaa Yassine, University of Paris 1

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0000.002
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 head0.362
Teacher spread0.327 · 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

Citations2,445
Published2015
Admission routes1
Has abstractyes

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