A Matter of Integration or Discrimination? Tracing the (Political) Use of Evidence in the Politics of Foreign Credential Recognition in Canada
Bibliographic record
Abstract
The problem of foreign credential recognition (FCR) in Canada has been widely researched by academics revealing three main explanations: admission policy, institutional complexities, and discrimination. While the problem of FCR has been documented in academia, little is known about government approaches to this issue. Using the literature on evidence-based policymaking as a theoretical basis, this research explores what evidence is used and how throughout the policy process addressing the problem of FCR. The main findings are fourfold. First, politicians are highly selective in their evidence use. Second, the range of evidence presented to politicians ultimately has little demonstrable impact on final policy decisions. Third, politicians add their own non-academic explanations for the problem of FCR to control its conceptualization. Fourth, politicians exhibit a political use of evidence in selecting only those supporting their original policy positions. Thus, this use of evidence reveals an interesting, yet hidden, exercise of power by politicians.
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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 | 0.032 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.021 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".