Framing environmental crises: Correlating action to outcomes for the 1969 Santa Barbara and 2010 Deepwater Horizon oil spills.
Bibliographic record
Abstract
The 1969 Santa Barbara oil spill was relatively small, yet generated significant society reverberations the 2010 Deepwater Horizon oil spill was unambiguously large, but resulted in only a few societal rumblings. Both were often labelled crises, disasters, and/or catastrophes (CDCs). Utilizing frame theory, this thesis analyzed whether a relationship existed between the use of strong rhetoric (i.e., CDCs) and action taken to respond to the spills, by establishing what various actors meant when they framed them as CDCs, and by ascertaining how their action-oriented CDC frames correlated with the actual outcomes. This thesis found that the actors meant a great number and variety of things by framing the spills as CDCs, and that only the term disaster had a significant number of correlations with the spills' outcomes. The results help explain why global environmental problems (e.g., climate change), despite being labelled crises, disasters, and catastrophes, are not receiving greater action. --Leaf iii.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".