Critical dialogical approach: A methodological direction for occupation-based social transformative work
Bibliographic record
Abstract
BACKGROUND: Calls for embracing the potential and responsibility of occupational therapy to address socio-political conditions that perpetuate occupational injustices have materialized in the literature. However, to reach beyond traditional frameworks informing practices, this social agenda requires the incorporation of diverse epistemological and methodological approaches to support action commensurate with social transformative goals. AIM: Our intent is to present a methodological approach that can help extend the ways of thinking or frameworks used in occupational therapy and science to support the ongoing development of practices with and for individuals and collectives affected by marginalizing conditions. METHOD: We describe the epistemological and theoretical underpinnings of a methodological approach drawing on Freire and Bakhtin's work. RESULTS: Integrating our shared experience taking part in an example study, we discuss the unique advantages of co-generating data using two methods aligned with this approach; dialogical interviews and critical reflexivity. DISCUSSION: Key considerations when employing this approach are presented, based on its proposed epistemological and theoretical stance and our shared experiences engaging in it. SIGNIFICANCE: A critical dialogical approach offers one way forward in expanding occupational therapy and science scholarship by promoting collaborative knowledge generation and examination of taken-for-granted understandings that shape individuals assumptions and actions.
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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 | 0.209 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.023 | 0.139 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".