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Record W2529845942 · doi:10.4000/insitu.13669

La discipline archivistique au Canada : état de développement et perspectives d’avenir

2016· article· fr· W2529845942 on OpenAlexaffabout
Carol Couture

Bibliographic record

VenueIn Situ · 2016
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsBibliothèque et Archives nationales du Québec
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Aujourd’hui, au Canada comme ailleurs, l’archivistique est devenue une profession, une discipline à part entière ayant droit de cité dans les universités, dans les cercles de recherche et dans la société en général. Pour appréhender le chemin parcouru, il n’y a qu’à considérer le foisonnement d’écrits – monographies, articles de revues professionnelles et scientifiques, recherches (rapports, mémoires et thèses réalisés par les étudiants) – et d’événements de toutes sortes (congrès, colloques, conférences et ateliers) qui animent les communautés archivistiques locales, nationales et internationales. Tout cela dans un contexte où le présent et l’avenir de la discipline archivistique, au plan national et international, sont fortement touchés par la « déferlante numérique » qui transforme et continuera de transformer de façon irréversible le quotidien de notre société. Si on nous demandait d’identifier l’élément le plus important du développement qu’a connu l’archivistique au cours des dernières années et qui marquera le futur de cette discipline, les technologies de l’information et leur impact sur la gestion de l’information feraient assurément l’unanimité.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.755
Threshold uncertainty score0.876

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.011
Science and technology studies0.0340.033
Scholarly communication0.0220.007
Open science0.0020.006
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0140.001

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 head0.090
GPT teacher head0.349
Teacher spread0.260 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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