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Record W3193557777 · doi:10.1177/14648849211036301

Journalism ethics with Foucault: Casually employed journalists’ constructions of professional integrity

2021· article· en· W3193557777 on OpenAlexaff
Mirjam Gollmitzer

Bibliographic record

VenueJournalism · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPrecarityJournalismPublic relationsSociologyPrecarious workWork (physics)SubjectivityCritical management studiesPolitical scienceMedia studiesGender studiesSocial science

Abstract

fetched live from OpenAlex

This article examines how journalists in non-permanent employment respond to their growing precarity. It is based on in-depth interviews with freelance journalists and interns who find that their working lives increasingly require entrepreneurial efforts. To work towards continuous access to journalistic work, these casually employed journalists engage in self-management and self-branding. To be able to make a living, they subsidize their income with work for clients outside of journalism that frequently offer better working conditions than news organizations but pursue narrow, strategic goals. The article develops a typology of non-journalistic work that illustrates that some non-journalistic jobs, but not others, cause these precarious news workers to defend their journalistic professional integrity. Furthermore, the study introduces Michel Foucault’s notions of the ‘entrepreneurial self’ and the ‘ethical self’ to interpret the different registers of professionalism between which journalists move today, identified as counter-, conforming and coping subjectivity. Thereby, the article uses a novel conceptual lens to make sense of resilience and change in journalistic professional identities under conditions of precarity.

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)0.015
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0170.113
Scholarly communication0.0140.011
Open science0.0010.008
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.413
Teacher spread0.310 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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