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Record W2202131998 · doi:10.3917/riges.403.0064

Collaborer pour faire le bien

2015· article· fr· W2202131998 on OpenAlexvenueaboutno aff
Liette d’Amours, Mathieu Ferland, Réal Jacob, Jean Poitras

Bibliographic record

VenueGestion · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArtPhilosophy

Abstract

fetched live from OpenAlex

Dix-huit mois : c’est le temps qu’il aura fallu à Ubisoft, à une équipe de recherche médicale de l’Université McGill et à leur partenaire américain pour réaliser une percée technologique, thérapeutique et bientôt commerciale. Comment expliquer ce rapprochement et, surtout, en expliquer la réussite ? Mathieu Ferland, producteur de jeux chez Ubisoft, suivi de Réal Jacob et de Jean Poitras, de HEC Montréal, nous exposent les apprentissages tirés de cette expérience. Et si une productivité et une capacité d’innovation accrues passaient par une telle collaboration intersectorielle ?

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)0.015
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.017
Scholarly communication0.0180.019
Open science0.0020.014
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0220.008

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 head0.046
GPT teacher head0.307
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2015
Admission routes2
Has abstractyes

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