Returning Trafficking Prevalence to the Public Policy Debate: Introduction to the Special Issue
Bibliographic record
Abstract
The clandestine nature of trafficking for sexual exploitation has prevented the development of reputable prevalence data, resulting in warring factions and battles over data that have hindered efforts to focus on combating trafficking. Articles in this special issue address the problem by discussing data sources and research practices that could help break the log jam. This introduction explains the policy agendas behind the prevalence wars. It also describes the many recent policy battles around legalization/decriminalization of the sex-trade industry that have broken out in the past few years in the European Union, Canada, Scotland, and the United Kingdom, where competing data assertions have been prevalent. Policy proposals made in advance of adequate knowledge about trafficking for sexual exploitation make new and better research a high priority.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".