Biopolitical Management, Economic Calculation and “Trafficked Women”
Bibliographic record
Abstract
Narratives surrounding human trafficking, especially trafficking in women for sex work, employ gendered and racialized tropes that have among their effects, a shrouding of women's economic decision-making and state collusion in benefiting from their labour. This paper explores the operation of these narratives in order to understand the ways in which they mask the economics of trafficking by sensationalizing the sexual and criminal aspects of it, which in turn allows the state to pursue political projects under the guise of a benevolent concern for trafficked women and/or protection of its own citizens. This paper will explore one national example: Article 18 of Italian Law 40 (1998). I argue that its passage has led to an increase in cooperation with criminal prosecution of traffickers largely because it approaches trafficked women as capable of making decisions about how and what they themselves want to do. This paper will also consider a more global approach to trafficking embedded in the concept of "migration management", an International Organization for Migration (IOM) framework that is now shaping EU, US and other national immigration laws and policies that impact trafficking. It will also examine the inherent limitations of both the national and global approach as an occasion to unpack how Article 18 and Migration Management function as forms of biopolitical management that participate in the production of "trafficking victims" into a massified population to be managed, rather than engender a more engaged discussion of what constitutes trafficking and how to redress it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".