The Interface of Mad Studies and Indigenous Ways of Knowing: Innovation, Co-Creation, and Decolonization
Bibliographic record
Abstract
This paper explores the interface between Mad Studies and Indigenous ways of knowing, and argues that the dialogical expanse that exists between these two fields could be a site for innovation, co-creation, and decolonization. Mad Studies is a radical approach to studying the ways we organize and respond to mental health experiences. The field questions and unsettles biomedical understandings of mental illness, and frames psychiatric experiences as diverse forms of human emotional or spiritual expression. Indigenous perspectives on disability describe mental health using a holistic, wellness-based lens, with many scholars highlighting the link to colonial violence and oppression. The interface of Mad Studies and Indigenous ways of knowing could provide a unique platform for gaining a broader understanding of Indigenous mental health while resisting Western, psy explanations of emotional distress. Different interpretations and understandings can be discussed and debated, and through ethical spaces (Ermine, 2007) new understandings or ideas may emerge. These, in turn, may help decolonize some of the dominant biomedical biases that underpin many contemporary psychiatric treatment approaches.Social workers have a particularly important role to play in these conversations. Our professional commitment to anti-oppression and social justice implores us to take an active role in these debates. Through our workplaces we can problematize dominant discourses from within dominant systems, and make our contribution to decolonization.
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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 | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.140 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.003 | 0.008 |
| 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".