Canada's Changing Immigration Policies: Report on Panel Discussion
Bibliographic record
Abstract
On November 19, 2014, a panel of experts convened at Ryerson University to discuss the consequences of recent developments in Canada’s immigration and settlement policies. These developments have been summarized in the RCIS Working Paper A Critical Review and Assessment of Canada’s Fast Changing Immigration Policies by Lotf Ali Jan Ali. The panel consisted of Ratna Omidvar, Executive Director of the Global Diversity Exchange; Morton Beiser, Professor of Distinction in Psychology; Gil Lan, Assistant Professor, Ted Rogers School of Management; and Naomi Alboim, Adjunct Professor at Queen’s University School of Policy Studies. The panel was chaired by Academic Director of RCIS, Harald Bauder. In this Research Brief, we summarize the main points of the discussion.¹
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.021 | 0.002 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".