MétaCan
Menu
Back to cohort
Record W2967583223 · doi:10.7202/1060813ar

Cartographier l’évolution du contenu de la revue Archives au moyen des techniques de fouille de textes et de bibliométrie

2019· article· fr· W2967583223 on OpenAlexaffvenueabout
Dominic Forest, Sabine Mas, Valérie Rioux, Vincent Larivière, Benoît Macaluso

Bibliographic record

VenueArchives · 2019
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophyArt

Abstract

fetched live from OpenAlex

À l’occasion du 50e anniversaire de l’Association des archivistes du Québec (AAQ), cinq professionnels de l’archivistique ont réalisé une analyse du contenu de la revue Archives à partir d’une approche basée sur des techniques de fouille de textes, complétée par une analyse bibliométrique. Ces techniques facilitent le traitement de grandes quantités de données afin d’en extraire automatiquement certaines caractéristiques qui les ont renseignés tant sur l’évolution thématique de la revue que sur la place qu’occupe sa production dans l’espace archivistique scientifique et professionnel québécois et international. Leur article offre une occasion de revenir sur les principales thématiques abordées dans la revue depuis les vingt dernières années en rendant compte de l’évolution des objets de recherche étudiés et, plus généralement, de la profession archivistique. Il permet également de s’interroger sur les liens existants entre les auteurs et leurs institutions, et de faire le point sur l’académisation ou la professionnalisation de la revue.

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)0.012
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.876

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0270.041
Science and technology studies0.0050.004
Scholarly communication0.0140.006
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.003

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 head0.069
GPT teacher head0.302
Teacher spread0.233 · 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
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

Citations0
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueArchivesSame topicCultural Insights and Digital ImpactsFrench-language works237,207