Challenging anthropocentrism and speciesism in social work education
Bibliographic record
Abstract
INTRODUCTION: Socially and legally acceptable views toward other animals are changing throughout the world. However, most social work education does not reflect such changes. Non-human animals are still viewed as tools for improving the wellbeing of human animals. To promote the development of social work education and practice responsive to today’s human and non-human animal relationships, this article discusses much-needed theoretical developments in social work education. APPROACH: We examine recent changes to the Canadian Association of Social Workers Code of Ethics and Canadian Social Work Education Accreditation Standards to assess the current frameworks for its education. These have recently added a focus on Environmental Justice, Sustainability and Ecological Practices to address growing concerns about climate change, yet do not consider animals explicitly or recognise the impacts of speciesism. Achieving a collective vision of social, economic, and environmental justice for all beings cannot be realised without considering non-human animals and actively challenging anthropocentric ontologies and epistemologies. CONCLUSIONS: We argue for a double-pronged approach addressing both ontologies and epistemologies of social work and discuss integrating key concepts from Critical Animal Studies (CAS) such as: anti-anthropocentrism, anti-speciesism, intersectionality, truncated narrative of dominance, and trans-species social justice, into social work education. By sharing authors’ teaching experiences, we demonstrate how such a theoretical orientation helps to critically analyse hierarchal relationships and envision practice to dismantle oppressive social systems that intersect with human and non-human animals. Thus, such theoretical changes, with a double-pronged approach in education, can strengthen social workers’ capacities to address justice.
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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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.121 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".