Do policies of the lowest common denominator bring about system-level change? Examining the success factors of the Toronto Regional Immigrant Employment Council
Bibliographic record
Abstract
The employment outcomes of Canadian immigrants have been deteriorating over the last two decades. Given the scope of the problem, a more systemic-based problem solving approach that involves multiple stakeholders is required to address immigrant labor market misfortunes in Canada. The Toronto Regional Immigrant Employment Council (TRIEC) has been recognized as a promising multi-stakeholder collaboration model in the area of immigrant economic integration. This study attempts to examine the conditions that contributed to the perceived success of TRIEC, with the goals being to discover critical success factors that made TRIEC a ‘success story’, and to identify lessons that could be learned by other Immigrant Employment Councils (IECs) in Canada. The study finds six success factor groups that were associated with TRIEC’s accomplishments. These include TRIEC’s highly professional operational practices, its action-oriented approach, its strong leadership model (which included business, community and municipal leaders), its focus on employers, its unique contextual environment, and its professional and highly effective communication and public relations strategies. It is argued that the last factor contributed to the formation of TRIEC’s ‘successful multi- stakeholder model’ branding in Canada and overseas. When the strategies and activities of TRIEC were compared to those of seven other IECs in Canada, it was revealed that TRIEC was the only IEC whose leaders either were or became truly vocal on the topic of immigrant integration. It was also discovered that TRIEC was the only IEC to openly and consistently share its results, activities and finances with the general public. Finally, TRIEC was the first group in Canada and only one of IECs that managed to bring three levels of government together to discuss immigration issues. Shedding light on the role of municipalities in relation to immigrant economic integration issues, this research has shown that all the Canadian municipalities of reviewed communities were recognized by IECs for their capacity to initiate and support collaborative community efforts. The final discussion focuses on the implications of solely adopting an economic perspective in promoting the benefits of hiring immigrants to Canadian society. The study concludes that more research should be conducted to define the success of multi-stakeholder collaborations such as TRIEC, to develop appropriate frameworks to measure their effectiveness and to evaluate what degree their interventions contributed to societal change(s).
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".