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Record W2779773055 · doi:10.7202/1042311ar

Résultats d’une enquête sur les pratiques et attitudes des chercheurs de l’Université Concordia en matière de gestion des données de recherche

2017· article· fr· W2779773055 on OpenAlexaffvenueabout
Danielle Dennie, Alex Guindon

Bibliographic record

VenueDocumentation et bibliothèques · 2017
Typearticle
Languagefr
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsConcordia University
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Selon les résultats d’un sondage et d’une série d’entrevues réalisés auprès de professeurs à l’Université Concordia à Montréal en 2015-2016 pour comprendre les besoins et les attitudes des chercheurs canadiens en matière de gestion des données, la majorité des chercheurs souhaitent mieux gérer, préserver et partager leurs données de recherche et reconnaissent les avantages de ce faire. En revanche, certaines contraintes les en empêchent, tels le manque de mesures incitatives ou de ressources humaines et technologiques, les problèmes associés à la confidentialité ou encore la volonté de garder un certain niveau de contrôle sur l’utilisation des données par autrui.

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)0.071
metaresearch head score (Gemma)0.141
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.141
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.014
Science and technology studies0.0080.003
Scholarly communication0.0100.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.002

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 head0.352
GPT teacher head0.449
Teacher spread0.097 · 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
Published2017
Admission routes3
Has abstractyes

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