Gérer la pratique, gommer l’éthique : analyse des chartes et codes pour encadrer les pratiques de dépublicitarisation
Bibliographic record
Abstract
Quel est l’encadrement éthique et déontologique de la publicité native, stratégie de dépublicitarisation payée, empruntant la forme du contenu éditorial de la plateforme qui l’accueille ? L’analyse de contenu de sept chartes et guides produits par quatre types de parties prenantes – marketing, information, consommateur, législatif – et de six entretiens fait émerger des positions en tension : stabilisation définitoire, réduction des enjeux éthiques, légitimation de la tromperie, dissuasion de tout encadrement extérieur à la profession. Ces axes soulignent un paradoxe : la standardisation des pratiques assure plutôt la prolifération de dispositifs ambivalents, tout en maintenant le cynisme face aux enjeux éthiques.
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 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 | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".