Beyond <i>Kafala</i>! Employers’ discriminatory attitudes and violations of the rights and freedoms of women migrant domestic workers in Lebanon
Bibliographic record
Abstract
Abstract Women migrant domestic workers (WMDWs) constitute 7.7 per cent of migrant workers worldwide, of whom more than a quarter work in the Arab region under the exploitative Kafala, system. In this article, we center employers as key actors in the making of the discursive meaning of Kafala. Utilizing data from a mixed-methods study on Lebanese employers of live-in WMDWs, we investigate whether practices that violate the rights and freedoms of workers are determined by the employers’ knowledge of what Kafala and local legal obligations entail versus other factors. The findings reveal that, although knowledge of legal obligations increases compliance with basic worker rights, employers shape the meaning of Kafala through practices that resonate with their discriminatory attitudes and financial interests. Thus, whilst benefit may accrue from enhancing employers’ knowledge of local legal obligations, only anti-racism advocacy that addresses deep-seated discriminatory attitudes and mobilizes employer morality would improve the rights and freedoms of WMDWs. The article does not abrogate Kafala as an exploitative system but calls for centering employers who, through their daily practices, contribute to shaping its meaning and reinforcing its power.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".