What do we mean by critical and ethical global engagement? Questions from a research partnership between universities in Canada and Rwanda
Bibliographic record
Abstract
Language - the words we use - can play a key role in enabling or limiting transformation of inequalities in the field of global health. At the same time, given the interdisciplinary, intersectoral, and international nature of much global health work, intended meanings, commitments, and underlying values for words used cannot be taken for granted. This commentary sets out to clarify, and in this manner render available for further discussion and debate, the phrase 'critical and ethical global engagement' (CEGE). It derives from discussions between scholars and partners in research, education, and healthcare practice based at one Canadian and two Rwanda institutions. Initially, our aim was to conceptualise the term 'critical and ethical global engagement' in order to guide our own practices. As the complexity of the values, commitments, and considerations underlying our use of this phrase emerged, however, we realised these discussions merited being captured and shared, to facilitate further exploration and exchange on this phrase.
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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 | 0.066 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.078 | 0.080 |
| Scholarly communication | 0.039 | 0.017 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".