Lessons on transformative resilience from migrant collective action in Toronto
Bibliographic record
Abstract
Goals for this community report We write this community report for migrant communities across Canada and the globe who are looking for ways to address the hardships associated with systemic inequalities through community-building and organizing. This report highlights how migrant communities tap into place-based and cultural knowledge to support each other and advocate for systemic change. Through documenting migrants’ capacity for individual, community and transformative resilience, we illustrate the often-unrecognized strength that migrants contribute to the countries where they settle. Through these case studies, we illustrate how migrant organizers link their personal struggles to broader social and political inequalities in this region. We also discuss how collective action promotes what one participant called “resilience, responsibility, and respect.” We hope that the lessons shared by migrant community leaders in this report will contribute to better understanding of the challenges migrant communities face. Through recognizing migrant’s contributions, we aim to foster greater appreciation for different forms of civic engagement that migrants bring through their critical understanding of social and economic inequalities they face in Canada and transnationally, and migrants’ capacity to bring about positive social change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".