Constance MacIntosh: Immigration Disability Discrimination
Bibliographic record
Abstract
Join Siobhan and Constance MacIntosh, Associate Professor at the Schulich School of Law, as they discuss disability discrimination in Canada's immigration system.\nConstance discusses the relationship between law and politics, how disability discrimination has been reflected in immigration policy over time, excessive demand provisions, and medical screening and inadmissibility.\nRead Constance's articles here:\nConstance MacIntosh, “Medical Inadmissibility, and Physically and Mentally Disabled Would-be Immigrants: Canada’s Story Continues” (2019) 42:1 DLJ 125.\nColin Grey, Constance MacIntosh & Sarah Marsden, “Canadian Immigration Law in the Face of a Volatile Politics” (2019) 42:1 DLJ i.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".