Bibliographic record
Abstract
The article discusses the challenges of data collection in the context of anti-human trafficking efforts in Canada. It aims to identify existing statistical data from government sources and stimulate discussions around data accuracy and availability. The analysis indicates that the available data predominantly focuses on crime-related statistics, highlighting the need for improved data practices. The article’s conceptual framework emphasizes open government as crucial for democratic governance, advocating for data availability and Access to Information (ATI) regimes that promote transparency and empower public engagement. It stresses that reliable data is vital for evidence-based policymaking, particularly in Canada, where responses to human trafficking have often been largely rhetorical and enforcement-centric. Structured in four parts, the article first outlines international standards for data collection on human trafficking. It then situates the research within open government principles, discusses the specific complexities of data reporting in Canada, and shares insights from the authors’ data collection experiences through ATI requests. The conclusion raises critical questions to guide future efforts in enhancing data collection and reporting processes related to human trafficking.
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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.023 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".