Bristish Petroleum America et la marée noire : cartographie stratégique de crise
Bibliographic record
Abstract
Notre recherche qualitative, une étude de cas en communication de crise, dresse le portrait de la marée noire dans le golfe du Mexique durant l’été 2010. La recherche est ancrée sur le modèle de l’Image Repair Theory (IRT) de W.L. Benoit, bonifié de contributions d’autres auteurs, afin d’étudier les stratégies de communication de crise utilisées par British Petroleum America (BP). \nEn scrutant la version Web de quotidiens d’information et le site Internet de BP, nous avons identifié 176 citations officielles que nous avons cataloguées grâce à une analyse de contenu basée sur la Théorie de la narration de Nicole D’Almeida.\nLa description de ce cas réel et l’analyse des stratégies de communication de crise de BP confirment l’adaptabilité de l’IRT bonifiée (IRTB) aux conditions de l’étude et au contexte propre à la crise. L’IRTB a facilité l’interprétation et l’indexation des six stratégies de communication de crise utilisées par BP afin de défendre, promouvoir ou réparer sa réputation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".