Moral residue and health justice for the global south: Addressing past issues through current interventions and research
Bibliographic record
Abstract
This paper introduces the concept of moral residue to global health, and shows how its presence undermines crucial interventions and research, especially in the global south. Lingering feelings of anxiety, anger, blame or frustration often exist among local populations, where previous interventions or research have left traces of harm and/or exploitation. The existence of such feelings reflects the presence of moral residue, recognizing the moral experiences of epistemic injustices, which in turn undermines critical interventions and research through outright rejection or passive non-compliance among affected populations. While such situations have been variously interpreted and relevant strategies developed to address the issues, little to no consideration is made on the implications of moral residue experiences in global health contexts and how to address them. This paper demonstrates the presence of moral residue in global health and proffers an African ethical approach, a harmony framework, for addressing moral residue issues, as part of a holistic approach towards tackling population health crises without compromising health gains for affected populations in the global south.
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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 | 0.044 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".