Perceived Employability and Gender Disparities in a Crisis: The Roles of ICT Use and Marital Status
Bibliographic record
Abstract
ABSTRACT In an increasingly digital world, understanding the relationship among information and communication technologies (ICTs), gender, marital status, and employability is crucial for advancing the United Nations Sustainable Development Goals (SDGs), particularly, gender equality. Disruptive crises, such as the COVID‐19 pandemic, pose significant challenges to this goal. This study examines the impact of ICT use and gender on low perceived employability during the pandemic, as well as the moderating effects of gender and marital status. Using data from 586 respondents in the United Arab Emirates (UAE), including students and workers, this study reveals that ICT use reduces low perceived employability, whereas being a woman is associated with greater perceived employability challenges. However, neither gender nor marital status was found to moderate the relationship between ICT use and perceived employability. This study contributes to research on gender equality, digitalization, and employability, providing evidence that while ICT use can enhance perceived employability during crises, women continue to face greater obstacles. These findings support the SDGs and provide valuable insights for managers, human resource management practitioners, and policymakers in promoting more inclusive and equitable workplaces.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".