Bibliographic record
Abstract
Throughout history, migration has been considered an indispensable part of human life. It has occurred due to various reasons, among which searching for new job opportunities has always been a chief one. This happens when workers observe lack of vacancies in their homeland, while there are chances for them in other regions and countries. It impels local workers to move to leave in search of better conditions in their careers, along with having the desire for a better life. However, divergent factors such as demographic change, socio-economic and political crises, and huge wage gaps between developed and developing countries play undeniable roles. Half of these migrants who move across borders for employment are women. Most of these women migrate from Asia especially India, Pakistan and Nepal to developed countries in the world to achieve new opportunities and a better life. The aim of this study is to identify the distribution of international female migrant workers around the world by applying compilation methodology approach, utilizing library documentation method and secondary analysis of qualitative data. The findings indicate that there is a strong connection between gender gap at work and the destination of female migrant workers. Nowadays, Northern, Southern and Western Europe and North America have the least gender gap between women and men at work. These regions are also the main destination of female international migrants. This research also predicts that in the future the destination of these women will be East Asia which is experiencing remarkable gender equality at work.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".