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Record W4390783378 · doi:10.3390/journalmedia5010004

The Datafication of Newsrooms: A Study on Data Journalism Practices in a British Newspaper

2024· article· en· W4390783378 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournalism and Media · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsNewspaperJournalismHabitusField (mathematics)SociologyCitizen journalismQualitative propertyPublic relationsMedia studiesPolitical scienceSocial scienceComputer scienceMathematics

Abstract

fetched live from OpenAlex

This study investigates the function of data journalism in a UK newsroom using Bourdieu’s field theory. The collection of study data was conducted through in-depth interviews, utilising a qualitative research methodology. The data obtained revealed that data journalism, a sub-field of journalism, continues to develop in an interdisciplinary structure and creates a new type of habitus (data habitus) within the field of journalism. This study also shows that the data journalism team in the newspaper has moved from being niche to being established as one of the most active and effective main sections of the newsroom, and that data-driven journalism has the potential to influence other teams. Lastly, this study suggested that the newsroom is undergoing a process of datafication by indicating the newspaper’s intention to develop data skills beyond the data journalism team.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.146
GPT teacher head0.422
Teacher spread0.276 · 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