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Record W2739412062 · doi:10.7202/1040389ar

Les prochains défis de la Division de la gestion de documents et des archives de l’Université de Montréal

2017· article· fr· W2739412062 on OpenAlexaffvenueabout
Diane Baillargeon

Bibliographic record

VenueArchives · 2017
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Depuis le début des années 1970, la Division de la gestion de documents et des archives de l’université de Montréal s’est imposée par son innovation, sa rigueur et son désir de communiquer son savoir et son savoir-faire. Reconnue internationalement pour l’exemplarité de ses méthodes de travail, l’article fait un retour sur les principales réalisations de la Division pour ensuite exposer les défis qui l’occuperont en terme de gouvernance informationnelle, de gestion des archives numériques et de valorisation des archives patrimoniales. L’auteure termine l’article avec les nouvelles définitions de la mission, de la vision et des valeurs de la Division ainsi qu’en soulignant l’importance d’entreprendre un exercice d’évaluation et de mesure de leurs services auprès de leurs partenaires.

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)0.024
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: Other · Consensus signal: Other
Teacher disagreement score0.764
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.011
Science and technology studies0.0150.017
Scholarly communication0.0230.008
Open science0.0030.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.002

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 head0.075
GPT teacher head0.310
Teacher spread0.235 · 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
GenreOther

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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Citations1
Published2017
Admission routes3
Has abstractyes

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