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Record W4307303363 · doi:10.22215/ff/v2.i1.02

Bonhommes de neige « épiques », sources expertes et rôles axés sur le public : Comment les rôles journalistiques se manifestent dans les médias canadiens

2022· article· fr· W4307303363 on OpenAlexaffabout
Nicole Blanchett, Colette Brin, Cheryl Vallender, Heather Rollwagen, Karen Owen, Lisa A. Taylor, Sama Nemat Allah, Kelti McGloin

Bibliographic record

Venuenot available
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsMount Royal UniversityNOSM UniversityUniversité de MontréalUniversité LavalOntario Centre of Excellence for Child and Youth Mental Health
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

En explorant les différences entre visions normatives et pratiques réelles (Mellado, 2020), grâce à une analyse de contenu de plus de 3700 articles d’actualité, contextualisée par une enquête auprès de journalistes canadiens et approfondie par des entretiens, cet article fournit un aperçu complet de la performance du rôle journalistique au Canada. Les résultats montrent des différences assez subtiles entre médias francophones et anglophones, une forte présence des journalistes canadiens dans leurs reportages, un niveau élevé du rôle d’infodivertissement. La production journalistique canadienne se démarque de celle des autres pays étudiés par l’importance des rôles civiques et de service; la place du rôle de chien de garde est toutefois moins grande dans les contenus que dans ce que rapportent les journalistes interrogés quant à leur conception personnelle du métier et leur perception de sa mise en oeuvre au sein de leur organisation.

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)0.011
metaresearch head score (Gemma)0.030
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: none
Teacher disagreement score0.764
Threshold uncertainty score0.469

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.009
Science and technology studies0.0130.014
Scholarly communication0.0260.015
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.131
GPT teacher head0.303
Teacher spread0.172 · 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
Published2022
Admission routes2
Has abstractyes

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