Race and Gender Analyses of Trafficking: A Case Study of Nigeria
Bibliographic record
Abstract
According to the Organization for Security and Cooperation in Europe human trafficking is currently a multibillion dollar business. This modern day form of slave trade does not only involve the transport of people across international borders but also the internal movement of people within regions and countries. From Asia to Eastern Europe from Latin America to Africa traffickers recruit victims who like commodities are smuggled within and across borders sold and then exploited under the threat of violence. Trafficking in persons is fuelled by development processes marked by class gender and ethnic concerns that marginalize women in particular from employment and education. As the overwhelming majority of trafficked persons are women and girls trafficking is usually considered a gender issue and the result of discrimination on the basis of sex. There has however been limited discussion of whether race or other forms of discrimination contribute to the likelihood of women becoming victims of trafficking. When attention is paid to which women are most at risk of being trafficked the link between this risk and their racial and social marginalization becomes clear. Race and racial discrimination have been found not only to constitute risk factor but may also determine the kind of treatment that women experience in destination countries. Moreover racist ideology and racial ethnic discrimination may create a demand in the region or country of destination which could contribute to trafficking in women and girls. (excerpt)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".