Analysis on Psychological Impacts due to Violation of the Rights of Women Workers
Bibliographic record
Abstract
This research aims to know the violations of the labour rights of women and analyze the psychological impact as a result of such breach. The results show there are some companies which do not meet the labor rights of women: 1) allow maternity and miscarriage leave to woman that is a labor contract but not giving salary; 2) grant leave of menstruation to woman that is a labor contract but not giving the salary and undermine the leave permit to woman that is a permanent labor; 3) do not give the time of breastfeeding at work; 4) does not provide a shuttle vehicle to woman labor at night; 5) no separated toilets for men and women. The Psychological impacts experienced by female labor are: 1) psychological problems experienced by female labor who suffer dilematic choices between the need of childbirth or miscarriage with economic needs; 2) female labor feel lost to get the chance of breastfeeding baby, feel guilt when daytime because no time for breasfeeding; 3) female contact who experience syndrome of premenstruation, they work even if uncomfortable, weak, and sick because if they are off then get no salary; 4) female labor feel afraid, threatened physically, of being the victims of sexual harassment on the way to or back from the company at night; 5) female labor feel uncomfortable, worried to men due to the adjacent toilet location.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".