Framing Migrant Resilience as a Civic Responsibility: A Case Study of Municipal and Provincial Immigrant Integration Policies in Toronto, Ontario
Bibliographic record
Abstract
Abstract This article presents a case study of how regional and municipal governments in Toronto, Ontario, use the concept of resilience to frame the challenges faced by immigrants and the steps governments are taking to promote immigrant integration. In the past decade, resilience has emerged as a policy framework to encourage positive adaptation of people and institutions that are facing social, economic and environmental challenges associated with population growth and economic globalisation. As a policy discourse, the concept of resilience is used to identify which immigrants need social and psychological support to better cope with pre- and post-migration stressors. Although government discourse acknowledges some of the structural inequities migrants face that require resilience (e.g. poverty, systemic racism, precarious employment), the discourse on migrant resilience notably omits government responsibility to enact structural solutions. Even the City of Toronto’s anti-racism campaign, which seeks to reduce racial bias and discrimination against immigrants, frames ‘civic resilience’ as an individual responsibility. Despite the promise of resilience to emphasise immigrants’ capabilities, we argue that resilience discourse operates as a type of diversity management strategy to identify which immigrants warrant government support to maximise their economic contributions to the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".