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Record W3136787380 · doi:10.7202/1075706ar

La contribution actuelle de la recherche doctorale en archivistique à l’Université de Montréal

2021· article· fr· W3136787380 on OpenAlexaffvenueabout
Simon Côté-Lapointe, Laure Amélie Guitard, Annaëlle Winand, Cécile Gaiffe

Bibliographic record

VenueArchives · 2021
Typearticle
Languagefr
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Si l’archivistique était auparavant, plus encore qu’aujourd’hui, associée à une pratique professionnelle, cette discipline s’est considérablement développée au Québec à travers la recherche universitaire. En témoigne le programme de doctorat à l’École de bibliothéconomie et des sciences de l’information (EBSI), créé en 1998, dont la première thèse portant sur un sujet relatif à l’archivistique a été octroyée en 2005 et la plus récente, fin 2019. Les projets en archivistique des actuels doctorants de l’EBSI reflètent le dynamisme de cette discipline et la multiplicité des avenues de recherche qui en découlent. C’est dans cette perspective globale que sont abordées chacune de ces recherches doctorales qui témoignent des évolutions de la contribution québécoise à l’archivistique actuelle.

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)0.015
metaresearch head score (Gemma)0.035
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 score0.994
Threshold uncertainty score0.645

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0110.009
Scholarly communication0.0150.006
Open science0.0020.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0410.004

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 head0.039
GPT teacher head0.253
Teacher spread0.214 · 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
Published2021
Admission routes3
Has abstractyes

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