The Municipal Role in Immigration
Bibliographic record
Abstract
As of June 2024, Canada’s population exceeded 41 million when including temporary foreign workers. Less than two years ago, the federal government announced that Canada would aim to welcome 500, 000 permanent residents per year by 2025, a growth in population that excluded temporary foreign workers, asylum seekers, and others. The vast majority of these new residents will reside in cities resulting in a rising demand for services to support and integrate newcomers. While many of the settlement services required fall within the purview of local government, municipalities do not enjoy any formal powers related to immigration, nor the funds to handle these increased pressures. The ninth report in the Who Does What series from the Institute on Municipal Finance and Governance (IMFG) and the Urban Policy Lab examines the role that municipalities play in immigration and their ability to fund, manage, and deliver services and implement policies to respond to new arrivals. Valerie Preston and John Shields show how non-governmental organizations and municipalities support international migrants, the complex links between them and the other orders of government, and the tensions and challenges in these relationships, using examples from Ontario. Valerie Pruegger explores the journey to get a municipal immigration policy approved by Calgary City Council, how it was subsequently implemented, and the various barriers encountered along the way. She identifies the strategies that were successful in overcoming these challenges. Mireille Paquet and Sivakamy Thayaalan examine what is needed to help municipalities handle the situation of residents who live with precarious immigration status or without any immigration status. They argue that municipalities need to have a role in the governance of immigration in Canada.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.034 | 0.016 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".