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Record W4287126756 · doi:10.5281/zenodo.4892718

Sondage sur la capacité des services institutionnels de gestion de données de recherche Rapport INSIGHTS no3 Avenir du soutien à la GDR pour les établissements : ressources priorisées, investissements, défis et accélérateurs

2021· report· fr· W4287126756 on OpenAlexaff
Alexandra Cooper, Lucia Costanzo, Dylanne Dearborn, Carol Perry, Andrea Szwajcer, Minglu Wang

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typereport
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversity of ManitobaYork UniversityUniversity of TorontoUniversity of GuelphQueen's University
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Un rapport sommaire des résultats du sondage a été publié en janvier 2020,[1] suivi du premier rapport Insights en juin 2020[2] et du deuxième rapport Insights en mars 2021[3]. Ce troisième et dernier rapport explore les sujets suivants en matière de développement des capacités de GDR : Ressources de GDR priorisées Investissements en technologie et en ressources humaines pour la GDR Barrières et défis au soutien à la GDR Accélérateurs de développement de la GDR

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)0.037
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.086
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.008
Science and technology studies0.0030.004
Scholarly communication0.0200.014
Open science0.0020.011
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0290.007

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 head0.441
GPT teacher head0.335
Teacher spread0.106 · 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
DomainReproducibility
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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicCultural Insights and Digital Impacts→French-language works237,207→