Merging arts and bioethics: An interdisciplinary experiment in cultural and scientific mediation
Bibliographic record
Abstract
How to engage the public in a reflection on the most pressing ethical issues of our time? What if part of the solution lies in adopting an interdisciplinary and collaborative strategy to shed light on critical issues in bioethics? An example is Art + Bioéthique, an innovative project that brought together bioethicists, art historians and artists with the aim of expressing bioethics through arts in order to convey the "sensitive" aspect of many health ethics issues. The aim of this project was threefold: 1) to identify and characterize mechanisms for the meeting of arts and bioethics; 2) to experiment with and co-construct a dialogue between arts and bioethics; and 3) to initiate a public discussion on bioethical issues through the blending of arts and bioethics. In connection with an exhibition held in March 2016 at the Espace Projet, a non-profit art space in Montréal (Canada), the project developed a platform that combined artworks, essays and cultural & scientific mediation activities related to the work of six duos of young bioethics researchers and emerging artists. Each duo worked on a variety of issues, such as the social inclusion of disabled people, the challenges of practical applications of nanomedicine and regenerative medicine, and a holistic approach to contemporary diseases. This project, which succeeded in stimulating an interdisciplinary dialogue and collaboration between bioethics and arts, is an example of an innovative approach to knowledge transfer that can move bioethics reflection into the public space.
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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 | 0.125 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.046 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".