A Complex Intergovernmental Problem: Why Asylum Seekers Challenge Canada’s Immigration System
Bibliographic record
Abstract
Abstract After years of collaboration in the immigration sector, Canada faces unprecedented intergovernmental tension due to a surge in irregular arrivals and asylum seekers. Most attribute this conflict to the scale of the arrivals. We argue it is the nature of asylum seekers as a “complex intergovernmental problem” that explains the recent turn in relations. Complex intergovernmental problems (CIPs) are a unique form of policy problem that result from a novel, boundary-spanning, intractable, and politically salient issue unfolding within an intergovernmental system that is ill-suited to manage the situation. In this article, we develop the concept of CIPs and trace how the archetypical example of asylum seekers has challenged the foundations of cooperative intergovernmental relations and driven conflict in Canada. Our account shows that starting in 2017 the immigration system responded with operational collaboration and ad hoc innovations. However, the system has failed to lock in these adaptations and build common norms on the respective roles of governments. The result has been escalating conflicts over resources and threats of service withdrawals. Our analysis suggests that adopting a problem-centric approach sensitive to the attributes of a policy challenge and how they interact with the existing intergovernmental system could enhance the understanding and management of CIPs, which are becoming more prevalent across federations.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.047 | 0.026 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".