A few “big players”: Systems approach to immigrant employment in a mid‐sized city
Bibliographic record
Abstract
Abstract Canada's immigration policy is regarded globally as a best practice model for selecting highly skilled migrants. Yet, upon arrival many immigrants face challenges integrating into employment. Where immigrants settle is one factor that has been shown to impact on employment integration. In Canada, regionalization policies have resulted in more immigrants settling in small to mid‐sized cities. It is important to understand how these local systems are organized to promote immigrant integration into employment. Using a systems approach, this paper presents a case study of immigrant employment in a mid‐sized city in Ontario, Canada. Through a document review and stakeholder interviews, a systems map was developed, and local perspectives were analyzed. Results demonstrate that in a mid‐sized city, few organizations play a large role in immigrant employment. The connections between these core organizations and the local labour market are complex. Any potential challenges to the system that interfere with these connections can cause a delay for newcomers seeking employment. As cities begin to experience growth driven by immigration, there is a need to ensure local services are not only available but also working effectively within the larger employment system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".