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Record W6969157160 · doi:10.5281/zenodo.5579827

Guide pour la curation dans Dataverse

2021· article· fr· W6969157160 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languagefr
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsMcMaster UniversityDalhousie UniversityUniversité de MontréalWilfrid Laurier UniversityQueen's University
Fundersnot available
KeywordsData curationContext (archaeology)Digital curation

Abstract

fetched live from OpenAlex

Ce guide offre des instructions en étapes pour la curation des nouveaux ensembles de données déposés dans Dataverse. Le guide est construit autour de l’acronyme CURATION pour aider les curateurs, surtout ceux qui commencent dans ce domaine, à se souvenir des étapes du processus de curation. Ce cadre est adapté des étapes CURATED du Data Curation Network en fonction d’un contexte bilingue.

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)0.009
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.991
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.004
Science and technology studies0.0030.002
Scholarly communication0.0090.008
Open science0.0030.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.1100.122

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 head0.111
GPT teacher head0.321
Teacher spread0.209 · 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 designNot applicable
DomainMethods
GenreMethods

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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