Addressing epistemic injustice (and ongoing effects of colonisation) through the Ethiopian intellectual tradition of <i>Qiné</i>
Bibliographic record
Abstract
As global health and medical education scholars build their understanding of the historical and continuing influences of colonisation, the absence of non-western forms of thought in medical education remains a challenge. Qiné is an Ethiopian intellectual tradition and poetic practice dating back many centuries (predating colonialism) that continues to exist and has the potential to expand scholarly inquiry in critical spaces. The central tenet of Qiné is that all phenomena, subject matter, knowledge and truth are incomplete and thus open for exploration and interpretation. In introducing Qiné in this analysis paper, we outline key Qiné definitions and concepts, describe our positionality and the processes we followed to bring Qiné concepts into this global critical scholarly space, provide a brief background on our Ethiopian/Canadian collaborative partnership model, review some of the literature about Qiné written in English and provide a few examples to illustrate the potential Qiné holds as a theory and methodology for global health and medical education. We conclude with some suggestions for next steps in incorporating Qiné into the methodological and theoretical toolkit for global critical scholarship. Advancing a-colonial theories and methodologies may be one effective way for educators and scholars to decolonise global health and medical education.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".