Skill Mismatch, Nepotism, Job Satisfaction, and Young Females in the MENA Region
Bibliographic record
Abstract
Skills utilization is an important factor affecting labor productivity and job satisfaction. This paper examines the effects of skills mismatch, nepotism, and gender discrimination on wages and job satisfaction in MENA workplaces. Gender discrimination implies social costs for firms due to higher turnover rates and lower retention levels. Young females suffer disproportionality from this than their male counterparts, resulting in a wider gender gap in the labor market at multiple levels. Therefore, we find that the skill mismatch problem appears to be more significant among specific demographic groups, such as females, immigrants, and ethnic minorities; it is also negatively correlated with job satisfaction and wages. We bridge the literature gap on youth skill mismatch’s main determinants, including nepotism, by showing evidence from some developing countries. Given the implied social costs associated with these practices and their impact on the labor market, we have compiled a list of policy recommendations that the government and relevant stakeholders should take to reduce these problems in the workplace. Therefore, we provide a guide to address MENA’s skill mismatch and improve overall job satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".