Domestic Violence and the Feminization of Homelessness in Malta: A Critical Perspective
Bibliographic record
Abstract
The majority of the homeless population in Malta are women and children. The main reason, I suggest, is the low status of women and the lack of power in the family bargaining process, which makes them vulnerable to abuse and violence by their intimate partners. After presenting evidence on Maltese women's low status, this article draws connections between the low position of women and the high prevalence of domestic violence and its resulted outcome: the feminization of homelessness. In addition to the published literature, this paper draws on three data sources: (1) the 2010 European Commission-sponsored survey, in which 26,800 citizens of EU-27 member states were interviewed regarding domestic violence (Eurobarometer 73.2, ‘Domestic Violence against Women’, 2010); (2) in-depth, interviews conducted with 1200 women from Malta and Gozo in February and March 2010 (Fsadni, The Prevalence of Domestic Violence against Women in Malta and its Impact on their Employment Prospects 2010); and (3) the results of in-depth interviews conducted by the author in June 2011, in Dar Merhba Bik, the main shelter for survivors of domestic violence. Although this study was able to find a significant association between Maltese women's disempowerment, domestic violence and resulted homelessness, additional work is required to see if this association is causal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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".