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Record W2154885740 · doi:10.7202/1002418ar

Les collaborations de recherche en génétique

2011· article· fr· W2154885740 on OpenAlexaffvenue
Robert Dalpé, Louise Bouchard

Bibliographic record

VenueCahiers de recherche sociologique · 2011
Typearticle
Languagefr
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsInstitute of Population and Public HealthUniversity of OttawaUniversité de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

La recherche en biotechnologie et en génétique est marquée par la constitution de grandes équipes de recherche et une préoccupation pour la commercialisation des résultats de recherche. Cette étude constitue une étude de cas sur la découverte de deux gènes responsables du cancer du sein et des ovaires (BRCA1 et BRCA2) dans les années 1990. Il s’agit des premiers gènes découverts pour une maladie fréquente et sévère. L’objectif est de comprendre la construction des grands réseaux de collaboration scientifique, notamment dans le contexte de la présence de l’industrie. Trois dimensions sont importantes pour expliquer ces réseaux. Premièrement, les particularités du secteur de recherche sont importantes, notamment les spécialisations des chercheurs. Deuxièmement, les stratégies et les objectifs de chercheurs sont déterminants. Au moment des grandes découvertes, les réseaux sont instables et les conflits émergent plus facilement. Troisièmement, les organisations incluant les politiques de financement expliquent aussi les stratégies des chercheurs.

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)0.055
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.085
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.019
Science and technology studies0.0070.012
Scholarly communication0.0200.013
Open science0.0020.013
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0180.004

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 head0.926
GPT teacher head0.656
Teacher spread0.270 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2011
Admission routes2
Has abstractyes

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