THE JOURNALISM STUDIES NETWORK (REJ) EXPERIENCE: a French-language experiment in collaborative research
Bibliographic record
Abstract
This article discusses the limitations and potentialities of journalism research utilizing the international networking model. It presents the experience of the Journalism Studies Network (REJ in the French/Portuguese acronym), a French-language research experiment, founded in 1999 and made up of 33 researchers linked to universities in Germany, Belgium, Brazil, Canada, France, Mexico and on Reunion Island. The report shows that despite the existing difficulties, the REJ takes the form of a place with relative autonomy in proposing objectives and methodologies for the study of journalism, which is reflected in the diversity of approaches and research interests developed within the Network. It also permits a more frank and level debate between researchers who occupy different positions in their careers. Coherence is also observed in the approaches adopted inside the REJ, which assures the continuity of the research programs developed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".