Strengthening critical allyship in social work education: opportunities in the context of #BlackLivesMatter and COVID-19
Bibliographic record
Abstract
COVID-19 has shifted social work education and widened the gaps in services for historically marginalised communities, including people of diverse cultural, sexual and gender identities and social classes. Existing inequities based on cultural differences have been magnified, perhaps most recently evident in George Floyd’s slaying and the subsequent #BlackLivesMatter demonstrations across the globe. Learning to be an ally for diverse communities and working towards the betterment of all people is a goal of social work education. We argue that simple allyship is not enough given the structural inequities present in North America and Australia the civil unrest amidst the COVID-19 pandemic. Social work education’s focus should trend towards allegiance with disadvantaged communities or critical allyship and include a commitment to undertake decisive actions to redress the entrenched colonial, capitalist, systemic and structural inequities that oppress many and provide unearned privilege and advantage to others. We explore strategies used in classrooms to promote allegiance and make recommendations for social work education, policy, and practice in this time of 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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.050 | 0.040 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.039 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".