Transforming settlement and integration services during a pandemic
Bibliographic record
Abstract
Abstract Settlement services are key to Canada's success in welcoming and integrating immigrants. Offered mainly in person prior to COVID‐19 by non‐governmental agencies reliant on and regulated by government funders, services were forced online and delivered by staff working remotely. We document this transition between September 2020 and September 2021 in Ontario, Canada and the conditions that influenced it. Surveys completed by workers and managers at member agencies of the Ontario Council of Agencies Serving Immigrants reveal how agencies provided services and stabilized organizational resources and capacities. Their success is evident in staff satisfaction with management's responses to the pandemic. While our findings underscore the resilience of the agencies and their workforce, they also challenge many tenets of New Public Management. The survey and discussions with managers suggest that sustained and flexible funding, rapid and respectful communication between agencies and funders and collaborations with other agencies were key to overcoming pandemic challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".