Reflections on ‘Welcoming’ Second- and Third-Tier Cities in Canada, Australia, New Zealand, and the United States
Bibliographic record
Abstract
Abstract Second- and third-tier cities in Canada, the United States, Australia, and New Zealand, have increasingly looked to international migration to offset the negative consequences of out-migration and labour market shortages. To make themselves more amenable to migrants, many communities have made deliberate efforts to become more welcoming. These efforts may take the form of narratives, policies, and practices that support diversity and inclusion. Welcoming initiatives have often had limited success, however, with many migrants still preferring to live in larger centres. This chapter provides cross-national comparative and analytical insights on the limitations of welcoming efforts in Canada, the US, Australia, and New Zealand. It argues that when welcoming is used as a means of attracting and retaining migrants in second- and third-tier cities, success may be limited due to the way welcoming initiatives are framed, systemic issues and inequalities increasingly faced by smaller cities, and inadequate attention to what is required for successful integration. The chapter calls for new ways of thinking about ‘welcoming’ cities and puts forward ideas for future research.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".