Ebola and Learning Lessons from Moral Failures: Who Cares about Ethics?: Table 1.
Bibliographic record
Abstract
The exercise of identifying lessons in the aftermath of a major public health emergency is of immense importance for the improvement of global public health emergency preparedness and response. Despite the persistence of the Ebola Virus Disease (EVD) outbreak in West Africa, it seems that the Ebola ‘lessons learned’ exercise is now in full swing. On our assessment, a significant shortcoming plagues recent articulations of lessons learned, particularly among those emerging from organizational reflections. In this article we argue that, despite not being recognized as such, the vast majority of lessons proffered in this literature should be understood as ethical lessons stemming from moral failures, and that any improvements in future global public health emergency preparedness and response are in large part dependent on acknowledging this fact and adjusting priorities, policies and practices accordingly such that they align with values that better ensure these moral failures are not repeated and that new moral failures do not arise. We cannot continue to fiddle at the margins without critically reflecting on our repeated moral failings and committing ourselves to a set of values that engenders an approach to global public health emergencies that embodies a sense of solidarity and global justice.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".