Researching, Monitoring, and Managing: Immigration Policy Work in Canada
Bibliographic record
Abstract
Based on interviews with bureaucrats involved in policy work at the Canadian immigration department, this article describes how their work may have influenced the content of policies between 2006 and 2015. In dialogue with historical accounts of bureaucratic immigration policy making in Canada and with concepts from the study of policy work, the findings highlight the importance of maintenance tasks (research, monitoring, and stakeholder management) and identify two pathways of bureaucratic influence: bringing problems to the agendas of decision makers and formulating solutions based on expertise. These results show that bureaucratic influence and the activities of a generally pro-immigration bureaucracy should be further explored as a contributor to Canadian immigration exceptionalism. They also shed a different light on patterns of immigration policymaking during the successive terms of the Canadian Conservative government (2005–2015).
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".