Canadian Post-Secondary Educational Institutions and Immigration Policy: Historical lessons and contemporary policy concerns
Bibliographic record
Abstract
Since 2008 post-secondary educational institutions (PSEI) in Canada have been increasingly implicated in the process of immigrant selection for the Canadian state. This is the result of changing immigration policies in Canada, and it is included as a stated goal in the most recent internationalization of higher education strategy documents developed at the provincial and national level (British Columbia Ministry of Advanced Education, 2012; Canada, 2014). This represents the emergence of a heterarchy (Ball, 2010), in which PSEI are being drawn into immigration policy regimes in which the larger assumptions and goals embedded within the policy are predetermined. This has profound implications for PSEI when one considers that throughout Canada’s history of immigration, actors in the immigration system have been required to balance the economic needs of the country and the socio-political desires of the population, and the result has often been exploitative or exclusionary immigration policies. For PSEI, this heterarchy exposes them to a host of ethical dilemmas of which they may not even be aware, nevermind equipped to resolve. This paper explores what the history of immigration can teach us about these potential ethical dangers for PSEI.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.034 | 0.023 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".