Les stratégies rhétoriques de légitimation des investisseurs institutionnels
Bibliographic record
Abstract
Cet article cherche à explorer les stratégies rhétoriques mobilisées par les investisseurs institutionnels afin de récupérer leur légitimité, fortement dégradée, suite à la crise financière de 2008. Les résultats sont révélateurs d’un court-termisme marqué, ainsi que de la très faible conscience de la responsabilité sociétale et de la finance durable que les responsables des investisseurs institutionnels ont ou devraient avoir. Les acteurs financiers n’ont consacré que 5 % de leurs discours écrits sur la gestion de risques pour le champ sociétal, durable, et relationnel. Notre étude a montré que le recours à un discours social et environnemental dans les documents de référence de la banque Société Générale représente des manœuvres rhétoriques mobilisées pour faire face à des pressions sociales et financières.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".