Trafficking in Women and the The Canadian
Bibliographic record
Abstract
En dPpit des pritentions d t ~ Canada gui se uoit le champion du monde en droits humains dans l'artne internationale, guand il s 'agit de protkger ces droits refatiuement au trafc desfemmes etauximigrantes, l 'auteure ne croit pas gui'f y ait matitre Li optimisme. Cetarticledinonce ks causes Li fa racine de l Fmigration et du trafc des femmes et demunde que Le gouuernement canadien repense radicalement son appui auxpo litigues de mondialisation et de libkraliration des marchks qu i sont rarement reconnuex respunsables de fa dktkrioration des conditions socio-kconomigues des pays du Sud auec comme rksultante, fa f i inisation de I'Pmigration. In the last five years, the phenom-enon of "trafficking in women " to Canada has emerged from relative obscurity to become one issue that has received considerable attention among Canadian governmental policymakers, law enforcement offi-cials, and academic scholars.' In 1996, when theGlobal Alliance Against Traffick in Women (GAATW) Canada began preliminary research aimed at developing acanadian pro-file of trafficking in women as part of an international report being com-piled by GAATW (Bangkok) and the
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".