Bayanihan during the COVID-19 pandemic: Grounding in community organizing and social movement praxis toward transformative social work
Bibliographic record
Abstract
This article analyzes ways to transition toward transformative social work by drawing from the historical, social, political, and economic contexts of Filipino migrant workers and their organizing praxis during and beyond the COVID-19 pandemic. This paper provides an overview of how the inequitable impacts on migrant and racialized populations by systems shaped around neoliberalism and racial capitalism only intensified the vulnerable conditions that Filipino migrants faced during the COVID-19 pandemic in Canada. Mutual aid networks burgeoned as a response to systemic failures and the need for communities themselves to provide basic needs, combat isolation, and advocate for systemic change. These networks build from existing forms of solidarity and support that communities often excluded from dominant discourses already have in place. These communities continually revitalize forms of collective action through cultural and local knowledge systems and histories of resistance, such as the Filipino notion of Bayanihan. The authors critically reflect on their participation in two mutual aid and community organizing initiatives during the COVID-19 pandemic drawing from Filipino epistemologies of Bayanihan and research on mutual aid, critical social work, community organizing, and social movements. Through these reflections, the authors unveil some of the practices and epistemological orientations that may help guide the profession toward its transformation by learning from community organizing and social movement praxis.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.070 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".