Public Event: Laya Behbahani: Human Trafficking In The Gulf States
Bibliographic record
Abstract
Laya Behbahani, who recently completed her MA in Criminology at SFU, will provide media accounts of the experiences of migrant workers in the Gulf Co-operation Council (GCC) states of the Middle East against a backdrop of the hybrid legal system and varying innovative, and often evasive, state responses in the GCC. A post-lecture dialogue will be moderated by SFU School of Communication's Adel Iskandar. The lecture will take place on January 31, 2017, at 7:00 PM at the Djavad Mowafaghian World Art Centre, Goldcorp Centre for the Arts. Co-presented by SFU's Vancity Office of Community Engagement, SFU's School for International Studies, the Institute for the Humanities at SFU, and the Global Communication MA Double Degree Program. Please see the attached PDF for details.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
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".