MétaCan
Menu
Back to cohort
Record W6912184218 · doi:10.5281/zenodo.2559256

Tri-Agency Research Data Management Policy Development

2019· article· en· W6912184218 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsNatural Sciences and Engineering Research Council
Fundersnot available
KeywordsData managementPresentation (obstetrics)Research dataPolicy developmentData collectionPublic policyManagement development

Abstract

fetched live from OpenAlex

Presentation on the Tri-Agency Research Data Management Policy for the Canadian Colleges, Institutes & Polytechnics Research Data Management Workshop.

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)0.148
metaresearch head score (Gemma)0.142
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.783

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1480.142
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0060.011
Science and technology studies0.0080.004
Scholarly communication0.0270.019
Open science0.0110.011
Research integrity0.0360.022
Insufficient payload (model declined to judge)0.1110.062

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 head0.200
GPT teacher head0.364
Teacher spread0.164 · 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
DomainReproducibility
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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicResearch Data Management PracticesFrench-language works237,207