Mobilizing Occupation for Social Transformation: Radical Resistance, Disruption, and Re-Configuration: Mobiliser l'occupation pour une transformation sociale : résistance radicale, perturbation et reconfiguration
Bibliographic record
Abstract
BACKGROUND.: Given the sociopolitical roots of widening occupational, social, and health inequities, it is imperative that occupational therapy move forward in mobilizing occupation for social transformation. PURPOSE.: Three key aims are addressed: articulating the imperative to mobilize occupation for social transformation; highlighting the political nature of occupation and occupational therapy; and providing guideposts for embracing a radical sensibility to inform moving forward in mobilizing occupation for social transformation. KEY ISSUES.: Conditions of possibility within occupational therapy leave the profession ill equipped to enact social transformation. Enacting calls to mobilize occupation for social transformation requires radically reconfiguring these conditions of possibility to inform practices that resist, disrupt, and re-configure sociopolitical conditions perpetuating occupational inequities. IMPLICATIONS.: Mobilizing occupation for social transformation holds much potential to contribute toward creating more equitable, humane societies. Realizing this potential involves committing to transforming our profession, as well as societal discourses, structures, systems, relations, and practices.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.068 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".