Enacting a critical decolonizing ethnographic approach in occupation-based research
Bibliographic record
Abstract
In response to calls for challenging coloniality and imperialism within occupational science, this paper outlines central tenets of decolonial theory and decolonizing methodological approaches to illustrate their relevance to transformative occupation-based research. Through describing the first-author’s dissertation work enacted in Tanzania, we illustrate how these principles unfolded through the design and enactment of a critical decolonizing ethnographic methodology in Tanzania. We unpack three tensions that were experienced in the field, share how these challenges were navigated, and discuss overall implications and risks of enacting decolonial approaches to research in occupational science. Ultimately, this paper aims to build upon existing decolonial scholarship within occupational science to further dialogue on both the necessities and tensions of enacting decolonizing approaches within occupation-based research, as well as highlight cautions for settler researchers engaging in such work.
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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 | 0.184 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.019 | 0.073 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".