Activist leadership in social work education: A recent Australian example that secured paid placements for students
Bibliographic record
Abstract
Abstract This article provides an account of the collective activism of four social work academic leaders who formed a coalition with students and trade union advocates to bring about one of the most significant progressive reforms to higher education in Australia in decades. Our campaign, involving a multipronged strategy, resulted in the Federal Government announcing a Commonwealth-funded ‘Prac Payment’ to help 68,000 students (studying social work, nursing, teaching, early childhood, and midwifery) with expenses during mandatory, unpaid placements. While not all goals have been achieved, our work continues, and our research, advocacy, media engagement, political lobbying, and alliance-building have initiated a national movement to assist students, particularly those from equity backgrounds, to complete their degrees and enter feminized professions with workforce shortages. The article describes our critical approach to activist leadership, which was inclusive and democratic; demonstrating antioppressive values in practice and a commitment to social justice by amplifying the voices of students with lived experience of placement poverty. Underpinned by activism theory and practice theory, the activist leadership in this campaign contributes to critical conceptions of social work leadership by shifting focus from individual leaders to leadership as practice, catalysing others to lead.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 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".