MétaCan
Menu
Back to cohort
Record W1912125069 · doi:10.7202/1028905ar

Enseigner les RDA : un défi de taille

2015· article· fr· W1912125069 on OpenAlexaffvenue
Nathalie Pilon

Bibliographic record

VenueDocumentation et bibliothèques · 2015
Typearticle
Languagefr
FieldComputer Science
TopicLibrary Science and Information Systems
Canadian institutionsCollège de Maisonneuve
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

L’arrivée de nouvelles normes représente toujours une période de grands changements pour un milieu documentaire. Le monde du catalogage connaît en ce moment de grands bouleversements avec l’arrivée d’un nouveau code de catalogage, les RDA (Ressources : description et accès). L’arrivée d’une nouvelle norme présente également des défis considérables pour les milieux d’enseignement. Cet article s’attarde aux défis soulevés par l’arrivée des RDA dans le domaine de l’enseignement formel.

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)0.080
metaresearch head score (Gemma)0.192
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.423

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.192
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.008
Science and technology studies0.0090.021
Scholarly communication0.0430.053
Open science0.0050.016
Research integrity0.0090.017
Insufficient payload (model declined to judge)0.0100.010

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 head0.060
GPT teacher head0.315
Teacher spread0.254 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations2
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueDocumentation et bibliothèquesSame topicLibrary Science and Information SystemsFrench-language works237,207