MétaCan
Menu
Back to cohort
Record W6946359867 · doi:10.34745/numerev_1760

Chercher l’humain dans l’institutionnel : étude des messages officiels publiés sur les sites web du gouvernement fédéral canadien

2019· article· fr· W6946359867 on OpenAlexaboutno aff

Bibliographic record

VenueNumeRev · 2019
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)PublicsPerformative utteranceWeb technology

Abstract

fetched live from OpenAlex

Résumé : Cet article présente une étude thématique de messages institutionnels publiés sur les sites web officiels des ministères et des agences fédérales au Canada. En adoptant une approche constitutive de la communication organisationnelle, elle s’intéresse à la manière dont ces messages projettent une logique institutionnelle particulière portée par des thèmes de référence qui donnent - ou non - de la visibilité aux publics internes et externes. Cette étude questionne ainsi l’application du principe de connectivité performative qui souhaite promouvoir une plus grande visibilité et présence des individus sur le web. Nos résultats montrent que les messages institutionnels projettent une logique composite portée par des thèmes sociaux et macro-organisationnels, qui désincarne fortement les communications sur le web. Les messages ne parviennent pas à appliquer le principe de connectivité performative exigé par les directives fédérales, ce qui remet en question la réussite de la refonte récente des sites web gouvernementaux.

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)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.943

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0130.005
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.002

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 head0.166
GPT teacher head0.274
Teacher spread0.108 · 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 designQualitative
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 routes1
Has abstractyes

Explore more

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