Bibliographic record
Abstract
These two statements one chosen from an anti-trafficking website and not dissimilar to most anti-trafficking discourses and the other qualifying nineteenth-century “white slaves” in Canada seem to be not only temporally but equally logically incompatible. Yet this paper will explore connections between the construction of women as “at risk” as potential victims and the effects that this construction entails on the appraisal of risk they themselves might be posing. Trafficking in women became part of the European concerns in the early nineties exclusively as a law-enforcement problem subsumed under illegal migration or organized crime. Women were therefore to be policed as illegal immigrants and rapidly deported. Victimization was put forth by various NGOs that felt it was the only way to further the human rights of women and prompt policy change both at the national and international level. Women were thus victims to be rescued rather than punished. The concept of victim was however not necessarily advantageous to women as it implies denial of agency and objectification of women (Doezema 1998 2001; Demleitner). A debate has ensued as the balance of the advantages and disadvantages of a victim approach is not self-evident. While this debate has been focused on the general victim (are women victims?) I propose to look at the specificity of victimization of trafficked women (which victims?). (excerpt)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".