Immigrant attraction and retention: An exploration of local government policies
Bibliographic record
Abstract
For cities, immigration is now considered a vital part of local economic and community development. Over the past half-century, many cities have experienced a series challenges caused by the impacts of late-stage demographic transition; the slow bleeding of skilled domestic workers to larger metropolitan areas; and the decline of traditional economic sectors. As a result, there has been a prioritization of attracting and retaining high-skilled and well-educated immigrants by local governments through locally-focused, place-based policies. Within this context, this paper examines the ways that cities in the Province of Ontario, Canada are constructing and implementing immigrant attraction, integration, and retention strategies. To achieve this goal, we identified and examined the local immigration policies of the 52 cities in Ontario, 36 of which have a formal immigration policy document. A comprehensive content analysis was conducted on these available to identify the ways that immigration is conceptualized, and the specific policies and approaches that local governments are implementing. Statistical analysis was used to determine if there was variation in policy across different types of cities. Based on this analysis, local governments are generally developing holistic, place-based policies – however, there is variation in approaches across cities of different sizes and geographies. These place-specific policies draw on local assets and advantages (i.e. existing migrant communities; local amenities and attractions; economic and education opportunities) while also work to enhance enhancing local capacity (i.e. building networks and immigration partnerships; training employers and city workers).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".