Towards Global Thinking in Bioethics: Hybridizing “Biology” and “Ethics”
Bibliographic record
Abstract
Abstract: Ethics as a field should, we argue, pay more attention to the (eco)system. Van Rensselaer Potter, one of the pillars of contemporary bioethics, advocated for a global “bios” ethics that literally and metaphorically bridges the gap between biological knowledge and ethical reflection. However, a Potterian Bio-Ethics faces a major obstacle: its acentric focus. Consequently, Global Bio-Ethics remains opaque for those trained under the anthropocentric biomedical ethics that instrumentalizes the environment. This paper aims to demystify two key concepts—Globality and Complexity—and show how a Potterian Bio-Ethics can effectively bridge the gap between knowledge-building in ecology and policy-making in the context of medical and environmental practices. Drawing on an experience in Québec, Canada, to mitigate the threat of antimicrobial resistance using a biosurveillance system, we show the utility of having ethics as an integral part of ecosystem(ic) approaches to bridge health and environment. We conclude by arguing that bioethics (and bioethicists) should become a locus for cascading communication, collaboration, and translation by co-constructing biosurveillance data governance.
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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 | 0.062 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.156 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".