MétaCan
Menu
Back to cohort
Record W4399917364 · doi:10.1177/10776990241246692

Spurring or Blurring Professional Standards? The Role of Digital Technology in Implementing Journalistic Role Ideals in Contemporary Newsrooms

2024· article· en· W4399917364 on OpenAlexaff
Cornelia Mothes, Claudia Mellado, Sandrine Boudana, Marju Himma-Kadakas, David Nolan, Karen McIntyre, Claudia Kozman, Daniel C. Hallin, Pauline Amiel, Colette Brin, Yi-Ning Katherine Chen, Sergey Davydov, Mariana De Maio, Filip Dingerkus, Rasha El-Ibiary, Maximiliano Frías Vázquez, Antje Glück, Miguel Garcés-Prettel, María Luisa Humanes, Sophie Lecheler, Misook Lee, Christi I-Hsuan Lin, Mireya Márquez-Ramírez, Jorge Maza-Córdova, Marco Mazzoni, Jacques Mick, Ana Milojević, Cristina Navarro, Dasniel Olivera Pérez, Marcela Pizarro, Fergal Quinn, Gonzalo Sarasqueta, Terje Skjerdal, Agnieszka Stępińska, Gabriella Szabó, Sarah Van Leuven

Bibliographic record

VenueJournalism & Mass Communication Quarterly · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversité Laval
FundersNational Research, Development and Innovation OfficeConselho Nacional de Desenvolvimento Científico e TecnológicoUniversidad Iberoamericana Ciudad de MéxicoPontificia Universidad Católica de ValparaísoNorthwestern University
KeywordsJournalismPolitical scienceMedia studiesSociologyPublic relations

Abstract

fetched live from OpenAlex

This study examines the perceived relevance and implementation of competing normative ideals in journalism in times of increasing use of digital technology in newsrooms. Based on survey and content analysis data from 37 countries, we found a small positive relationship between the use of digital research tools and “watchdog” performance. However, a stronger and negative relationship emerged between the use of digital audience analytics and the performance of “watchdog” and “civic” roles, leading to an overall increase in conception–performance gaps on both roles. Moreover, journalists’ use of digital community tools was more strongly and positively associated with “infotainment” and “interventionism.”

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)0.014
metaresearch head score (Gemma)0.055
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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.012
Scholarly communication0.0120.006
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.369
Teacher spread0.337 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournalism & Mass Communication QuarterlySame topicMedia Studies and CommunicationFrench-language works237,207