Immigration Policy and Less-Favoured Regions and Cities: Comparing Urban Atlantic Canada and the US Rust Belt
Bibliographic record
Abstract
Abstract There is a growing interest in immigrant receiving countries like Canada and the United States in spreading the benefits of immigration to less-favoured regions and cities that face a myriad of demographic and economic challenges associated with aging or shrinking populations, slow growth, and economic decline. This chapter uses the cases of Atlantic Canada and the US Rust Belt to examine two different approaches to immigration and uneven development. In Canada, place-based immigration programmes explicitly encourage immigration to Atlantic Canada while immigrant integration is supported through ‘top-down’ federally-funded settlement, multiculturalism, and citizenship programmes. Conversely, in the US, efforts to use immigration to address spatial inequality are happening outside of formal policy channels from the ‘bottom-up’, driven by networks of local business associations and non-profit organisations that increasingly promote immigration as a tool of economic revitalisation in the Rust Belt. Drawing on several years of fieldwork in both regions involving participant observation at immigration summits and conventions, stakeholder interviews, and media and document analysis, this chapter considers the implications of these diverging approaches. Ultimately, dynamic regions need dynamic solutions, and cities in these regions provide a roadmap for understanding the role of immigration in addressing uneven development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".