Bibliographic record
Abstract
Carried out as part of an educational project by Hugo Saez under the supervision of Doina Muresanu, this case study focuses on ethical aspects linked to the field of journalism. It can be used for undergraduate or graduate students in the field of organizational management or communication. The present case study takes as a subject of analysis the situation of the French media, given Hugo's work experience in this profession and in this country. For this reason, the case is written in the first person. More concretely, students will learn about the regulations governing this profession in France, the misconduct that continues to persist in the field, and end with lessons to be learned. After having gone through the description of the situation, students are invited to think about questions relating to ethical issues linked to the French media sphere, to the errors of conduct encountered when searching for information as well as to causes which could explain the distancing of journalism from its ethical principles anchored in practice for several decades. Basically, the case study aims to raise students' awareness of issues related to the study of applied ethics in a particular context, which is the practice of journalism in France.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| 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".