Combating gender-based violence related to refugee women
Bibliographic record
Abstract
Feminist media studies have been conducted to examine how stereotypes created by mass media contribute to popular understandings of rape culture. Because of a lack of empirical researches on gender violence In the context of migration, this article seeks to understand how the representation of refugee women In media may reinforce stigmas associated with immigration. Based on the researches of media framing and coverage of violence against women, we focused on examining the stereotypes present In this area of research, adopting the principles of Intersectional feminist media analysis. In order to achieve this goal, this study used the method of feminist critical discourse analysis, focusing on three levels of analysis: lexical-semantic, enunciative and rhetorical. The study is based on a news article published by the newspaper Le Monde on September 18, 2023. The findings revealed a dichotomy In the representation of women, highlighting the active voice of French workers, as opposed to the tendency to portray refugee women as passive victims of violence. Furthermore, the functions of the discourse reported In the news, with emphases on nationality and the violence suffered by these women, contribute to the construction of a certain portrait of these women while ignoring the centrality of their condition as asylum seekers In France.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".