Deservingness by Design? Temporal Governance in the Canadian Immigration System.
Bibliographic record
Abstract
The scholarly fields of geography and sociology have identified time as an important factor shaping the policy design that instruct the flow of migrants, however this phenomenon remains sparsely investigated in the political science discourse. Considering this gap in the literature of migration studies and political science, this thesis operationalizes temporal governance in the Canadian policy design context as a tool of control and investigates its differential application in the categories of skilled migrants, family sponsorship and refugees and asylum seekers. To address this gap, I use Melanie Griffith’s coined term, “temporal governance” to assert that time is used as a policy tool to design permissive and restrictive eligibility criteria and it is used differently across Canada’s permanent immigration categories: economic migration, family sponsorship and refugee sponsorship. Second, I use Schneider and Ingram’s social constructions theory to assert that the temporally permissive and restrictive policy design reveals that the economic migration receives permissive policy treatment based on their positive social constructions and usefulness in achieving the state’s economic immigration objectives. Whereas family sponsorship and refugee sponsorship receive restrictive policy treatment due to their weak political associations. Ultimately, this policy design decides which categories are more “deserving by design”.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.026 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".