Intersections between Human Trafficking and Education: Toward New Research Agendas
Bibliographic record
Abstract
Human trafficking is a social issue that has gained attention in the media and in scholarship. A growing number of anti-trafficking organizations and actors have begun to use education to diverse ends. Although not often associated with human trafficking, education is a common tool used by anti-trafficking organizations, whether as a prevention tool to reduce the vulnerability of people at-risk of trafficking, or as a service to trafficking survivors to improve their lives. Lack of access to education, or to quality education, is also a factor in exposure to human trafficking, whether that be in terms of debt bondage, domestic servitude, forced labor, child marriage or other issues related to human trafficking. More explicit connections need to be made between the work being done in anti-trafficking spheres and the scholarship of education in order to better understand how to improve the quality and effectiveness of education-related efforts. This essay explores the connections between human trafficking and education.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 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".