Vulnerability, Legal Protection, and Work Conditions of Female Domestic Workers in Addis Ababa, Ethiopia
Bibliographic record
Abstract
Domestic workers are one of the most vulnerable groups of workers. In Ethiopia, however, the vulnerability, legal protection, and work conditions of female domestic workers are not well-documented and researched. Hence, the purpose of this study was to investigate the vulnerability, legal protection, and work conditions of female domestic workers in Addis Ababa, Ethiopia. A cross-sectional qualitative research design was employed using in-depth interviews, key informant interviews, and focus group discussions. The study participants were 15 domestic workers, three officials from the Office of Labor and Social Affairs, and five brokers of domestic workers. Findings indicate that female domestic workers experienced abuse in various forms including verbal or psychological abuse, physical abuse, and sexual assault. Female domestic workers in the study area had few or no labor rights or protection. They rarely had clear contractual relations, worked long hours for low pay, and had little or no privacy. There is neither a proper state institution to promote the rights of domestic workers nor a strong viable movement among or on behalf of domestic workers. Hence, a relevant legislative framework developed by the city and national governments, and strong advocacy efforts to expose their working conditions are needed to improve the work conditions of female domestic workers.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".