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Record W4411703533 · doi:10.1093/bjsw/bcaf115

Activist leadership in social work education: A recent Australian example that secured paid placements for students

2025· article· en· W4411703533 on OpenAlexaff
Christine Morley, Linda Briskman, Lynne Keevers, Wendy Bowles

Bibliographic record

VenueThe British Journal of Social Work · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsVictoria Park
FundersQueensland University of Technology
KeywordsWork (physics)Public relationsSociologySocial workPedagogyMedical educationPolitical scienceEngineeringMedicineLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0070.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.182
GPT teacher head0.426
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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