Bibliographic record
Abstract
L’article esquisse une réflexion critique sur ce que l’on appelle communément la communication d’influence, afin de préciser le relief de cet objet et, plus fondamentalement, d’esquisser un renversement de perspective. Il commence par objectiver les présupposés qui sous-tendent l’approche usuelle de la communication comme processus d’influence ; prenant le cas des « influenceurs » sur les réseaux sociaux, il explicite ainsi certaines illusions comme la croyance en une efficacité persuasive de la communication. En contrepoint, il analyse l’extension des pratiques de communication dans le cadre de processus rationalisés d’influence à des fins économiques et politiques. C’est par conséquent le vaste domaine du lobbying, des affaires publiques et des actions diverses et variées menées par des groupes d’intérêts qui s’imposent à l’analyse parce qu’ils constituent le cœur de la communication d’influence et le principe de son développement. La maîtrise et le contrôle de la communication apparaissent ainsi comme des enjeux majeurs, stratégiques à l’échelle de la société, parce qu’ils conditionnent, au-delà de la prévalence d’intérêts particuliers, la domination.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".