Employing the evidence: Building employment and entrepreneurial opportunities for refugee newcomers through community-based settlement and inclusion practices
Bibliographic record
Abstract
Brockville, Ontario, a community of 22,000 people along the bank of the St. Lawrence River in Eastern Ontario, has welcomed almost 80 refugee newcomers in three years' time. About half of these individuals are adults and half are children. The community was relatively unprepared for such a large influx of newcomers all at once – services and supports are largely in place for those newcomers who 'trickle in' as immigrants. This investigation looks at how the community can better support and provide service to those newcomers who have arrived with vast skill sets and so far, underutilized talents. Using a 2016 study into the best practices for implementing programming and services for assisting newcomers – specifically immigrants – in entrepreneurship, as well as attracting those entrepreneurs, this study looks at two central recommendations to assist in the building of entrepreneurial skill among these working age newcomers. This investigation finds that although the recommendations were originally made for the City of Brockville to implement, community-based groups – especially Refugees for Brockville – can collaborate with existing agencies to initiate programming that is culturally connected and community-integrated and will assist refugee newcomers on their professional trajectories regarding entrepreneurship and beyond.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".