Transformative and Restorative Processes: Revisiting the Question of Efficacy of Indigenous Healing
Bibliographic record
Abstract
Studies of the efficacy of 'traditional' Indigenous healing often fail to consider the epistemologies that underlay specific healing traditions, especially intrinsic notions of efficacy. In this article, I critically engage the concept of efficacy by identifying two somewhat different approaches to the issue of outcome. In 'transformative' healing processes, healing is conceptualized as a journey in which the outcome goal is a transformed individual. Efficacy, then, is about incremental changes toward this goal. In 'restorative' healing processes, the goal is termination of the sickness and the restoration of health; efficacy is conceptualized as a return to a presickness state. These healing processes are illustrated with examples from the Q'eqchi Maya of Belize and Aboriginal peoples of Canada.
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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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.132 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".