Enacting contextually responsive scholarship: Centering occupation in participatory action research with children in India
Bibliographic record
Abstract
Occupation-based scholars are striving to mobilize socially responsive scholarship to address occupational injustices from local to global scales. Moving forward involves expanding beyond Western, Anglophonic, female, able-bodied, adult perspectives on occupation, with critically informed participatory methodologies providing one key means to incorporate diverse perspectives on occupation and occupational justice. Drawing upon a participatory action research project with children with disabilities from rural South India, this paper puts forward an understanding of participatory action research as an occupational process (i.e., embodying a variety of occupations) and an occupation-based process (i.e., informed by an occupational lens). We forefront how 'occupation' was centered and mobilized within the process of this participatory action research. In addition, drawing on select study findings, we illustrate and discuss how participatory action research provided a forum to partner with the child co-researchers and collaboratively identify and critically analyse occupational injustices. Through this illustration and discussion of participatory action research as an occupation-based process and occupational process, we demonstrate its potential to be used to enact contextually responsive scholarship and praxis. Key words: Occupation-Based Participatory Action Research; Occupational Justice; Occupation-Based Transformation; Participatory Filmmaking
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".