Rendre des comptes : comment situer l’entreprise dans le discours démocratique?
Bibliographic record
Abstract
Dans cet article, l'auteur s'inspirant des débats récents soulevés par les poursuites stratégiques contre la mobilisation publique ou encore poursuites-bâillons, propose certaines pistes de réflexion sur la façon dont les entreprises s'insèrent dans le débat public et démocratique. Il tente tout d'abord de présenter les configurations du « processus de reddition de comptes » de plus en plus complexe dans lequel sont impliquées les entreprises. Ensuite, il tente d'identifier certains modes de participation au débat public en se penchant notamment sur la distinction entre discours commercial et discours politique. Enfin, il termine en proposant quelques réflexions sur les leçons qu'il semble possible de tirer de ces débats sur nos façons de penser l'idée de « responsabilité sociale des entreprises ».
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".