MétaCan
Menu
Back to cohort
Record W3082390975 · doi:10.1080/17512786.2020.1813049

“I Knew I Wouldn’t be Well Remunerated Before my 30s”: Professional Transition in French Journalism

2020· article· en· W3082390975 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournalism Practice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité du Québec à Montréal
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsJournalismSet (abstract data type)Identity (music)Public relationsOrder (exchange)Political scienceTransition (genetics)Digital eraDigital mediaSociologyMedia studiesBusinessLawComputer scienceThe Internet

Abstract

fetched live from OpenAlex

This article analyzes digital journalists and their entry into French journalism. It combines labor market data with a description of journalistic careers. The study explores the choices made by online journalists in a scenario where employment is decreasing and uncertainties about the future are growing. In order to deal with this situation, French journalists have developed a set of strategies (increased training, more pre-professional experiences, developing skills in digital journalism, and international coverage) that help mitigate the uncertainty around entering the profession. At the same time, the choice to become a journalist in an adverse scenario such as this shows a strong commitment to journalism and a strong adherence to a set of relatively stable features of identity discourse.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.002
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 head0.176
GPT teacher head0.474
Teacher spread0.298 · 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