Proposed Principles for International Bioethics Conferencing: Anti-Discriminatory, Global, and Inclusive
Bibliographic record
Abstract
This paper opens a critical conversation about the ethics of international bioethics conferencing and proposes principles that commit to being anti-discriminatory, global, and inclusive. We launch this conversation in the Section, Case Study, with a case example involving the International Association of Bioethics’ (IAB’s) selection of Qatar to host the 2024 World Congress of Bioethics. IAB’s choice of Qatar sparked controversy. We believe it also may reveal deeper issues of Islamophobia in bioethics. The Section, Principles for International Bioethics Conferencing, sets forth and defends proposed principles for international bioethics conferencing. The Section, Applying Principles to Site Selection applies the proposed principles to the case example. The Section, Applying Principles Beyond Site Selection addresses other applications of the proposed principles. The Section, Objections responds to objections. We close (in the Section, Conclusion) by calling for a wider discussion of our proposed principles.One-Sentence Capsule Summary: How should bioethicists navigate the ethics of global bioethics conferencing?
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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 | 0.083 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.042 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".