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Record W6983275894

L'utilisation de modèles ouverts de collaboration dans le cadre de la recherche en génétique humaine : promouvoir la vertu par l'innovation sociale

2009· other· en· W6983275894 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2009
Typeother
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyVirtueIdeal (ethics)IndividualismFlexibility (engineering)Property (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.082
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.997
Threshold uncertainty score0.435

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.075
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0120.070
Scholarly communication0.0290.027
Open science0.0030.026
Research integrity0.0100.010
Insufficient payload (model declined to judge)0.0080.003

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.

Opus teacher head0.042
GPT teacher head0.280
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueLibrary and Archives Canada (Government of Canada)Same topicdemographic modeling and climate adaptationFrench-language works237,207