L'utilisation de modèles ouverts de collaboration dans le cadre de la recherche en génétique humaine : promouvoir la vertu par l'innovation sociale
Bibliographic record
Abstract
In the field of information technology, the open source approach has offered programmers an alternative based on collaboration, to the traditional, individualistic and proprietary model – supported by the use of intellectual property – of developing software. This alternative has not only a considerable utilitarian appeal; it could also make it possible to promote greater human flourishing, in the sense of virtue ethics, by encouraging the development of numerous virtues in its participants. This potential advantage deserves to be studied in greater detail, because it is of great interest at a time where a growing number of academic researchers have critiqued the increasing focus of intellectual property law on the promotion of economic development at the expense of certain other valuable societal goals. The present thesis will thus have, as a central objective, to demonstrate that in a given human practice (that of human genetic research), the development of open source projects can promote the development of virtue in contributing collaborators. Secondarily, it will also be shown that intellectual property is not always generating an ideal scenario in the field of human genetics. This second finding suggests that a strategic recourse to the open source model could be defended by arguments of a more utilitarian nature as well. If human genetic research is properly directed and confirms its current potential, it could make it possible to better inform doctors about disease functioning processes, provide superior predictive tests, offer optimised medical treatments and even, replace failing organs. Considering the enormous therapeutic potential, but also the concerns raised by this research, it would be important that scientists who are involved in genetic research become more preoccupied by the human aspect of the equation, without necessarily neglecting economic profit. The development of a more vir
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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 | 0.082 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.070 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".