Breaking Isolation: Social Work in Solidarity with Migrant Workers through and beyond COVID-19
Bibliographic record
Abstract
In the early months of COVID-19's proliferation through Canadian communities, the now largely documented uneven impacts and burdens of the illness were emerging. Among the early COVID-19 casualties were workers in Alberta's meatpacking plants, with infection rates so high that the news quickly gained international attention. The Cargill meatpacking plant, overwhelmingly staffed by temporary foreign workers with no permanent status or citizenship rights, was the site of the largest single coronavirus outbreak in Canada. The need for a community response to this emerging crisis was a focal discussion for a newly formed network of social workers. A multileveled series of actions and systems advocacy were put in place. These actions would foment a vibrant and diverse "community of communities" while also unveiling challenges and obstacles to the work during a period of a shifting health landscape, shutdowns, and changing legislation. This article focuses on the development of a grassroots and transformative community-led response to COVID-19, describing strategies, implementation, and challenges in the "real life" context of the recent pandemic. Key learnings for postpandemic community organizing and social work solidarity actions are highlighted.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.051 | 0.037 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".