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Record W7101449911 · doi:10.5281/zenodo.17460096

Compétences collaboratives pour la recherche et le développement responsable des technologies quantiques - Note de synthèse

2025· report· fr· W7101449911 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typereport
Languagefr
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversité LavalUniversité de Sherbrooke
Fundersnot available
KeywordsContext (archaeology)Emerging technologiesAccess to information

Abstract

fetched live from OpenAlex

Ce document constitue une note de synthèse du colloque "Compétences collaboratives pour la recherche et le développement responsable des technologies quantiques" (Colloque 17) tenu le 6 mai 2025 lors du 92ème Congrès de l'Acfas à Montréal, Québec, Canada. Le colloque a exploré le rôle des compétences collaboratives comme pont entre les disciplines dans le développement responsable des technologies quantiques. La journée s'est articulée autour de quatre sessions : un panel d'experts sur les défis interdisciplinaires, un atelier sur les situations-types de collaboration, des présentations sur la formation aux compétences collaboratives, et un atelier d'idéation sur les outils et méthodes collaboratives. Les discussions ont mis en évidence l'importance de la collaboration interdisciplinaire et intersectorielle, tout en identifiant des défis spécifiques aux technologies quantiques : complexité technique, enjeux de souveraineté, évolution rapide, et nécessité de développer l'intelligence émotionnelle. Le document propose plusieurs niveaux de formation pour soutenir le développement de ces compétences chez les chercheurs, formateurs et acteurs industriels. Document issu du projet Dialogues Quantiques (2023-2026).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.061
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.441
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0060.002
Scholarly communication0.0060.001
Open science0.0040.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.439
GPT teacher head0.472
Teacher spread0.033 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes2
Has abstractyes

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