Occupational (Therapy's) Possibilities: A Queer Reflection on the Tangled Threads of Oppression and Our Collective Liberation
Bibliographic record
Abstract
This presentation stems from the work of occupational therapy and science scholars who have critically described how systems of dominance perpetuate health inequities and limit the occupational possibilities of those we aim to support. Liberation is discussed as a communal process and outcome of untangling, undoing, and reconfiguring systems of dominance that negatively impact health and limit the occupational possibilities of individuals, groups, and communities. In critically reflecting on my personal, professional, and ongoing journey toward liberation as a gay, white, able-bodied, man, I draw parallels between the systemic and intersecting oppressive forces that limit the occupational possibilities of historically marginalized groups and the need for our profession to consider its own liberation. Informed by queer theory, I question the binary discourses that separate the "Us" from the "Them," illustrating how our struggles to transform practice based on anti-oppressive principles and the liberation of our full potential as occupational therapists must be tied to the liberation of the communities we aim to support. Drawing on lessons from liberation movements, I argue for the necessity of a representative and compassionate professional community to support collective action and to position the celebration of communal achievements as resistance and acts of gratitude.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".