Bibliographic record
Abstract
The phenomenon of human trafficking stands out as the starkest example of illegal border crossing. Illegal migration with a difference – trafficking has “victims.” As victims, those who are trafficked fit differently into the imagination than many of those who are rendered illegal by the migration laws of prosperous nations. The label “illegal” will hardly stick, as the victims are innocent. This makes it more difficult for states to rhetorically cast the victims of trafficking as transgressors, thus altering the familiar illegal migration discourse. It is even problematic to equate the victims of human trafficking with those who suffer from the trafficking of drugs. Drug addicts are discursively more blameworthy than human trafficking victims are; they somehow are associated with some initial choice, a lack of willpower, or at the very least, a deep weakness. Trafficked humans have none of these markers. Although people trafficked are not only women and children, they are overwhelmingly so. In addition, while they do not exclusively serve the sex trade of the prosperous, sex work and sexual exploitation predominate as explanations for the success, in market terms, of trafficking in human beings. The importance of human trafficking to globalized migration laws is all about victims: what it means to be a victim, how victims are constructed and named, how victims shape criminals, and how victims call forth remedy, or lack the power to do so. More than refugees, the victims of trafficking trouble the insider-outsider dichotomy of migration law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".