Death by a thousand spills: How corporate branding and media strategies downplay the risk of offshore oil spills in Canada
Bibliographic record
Abstract
On 15 November 2018, Newfoundland experienced its largest oil spill. The disaster saw the SeaRose platform disperse 250,000 litres of oil into the Atlantic. Despite the accident’s unprecedented nature, Husky Energy (the company responsible for the spill) minimized the public’s perception of potential ecological risks by transforming the disaster into an everyday fact of life. Focusing on Husky’s mediation of the spill, this article shows how Husky’s visual representation of small offshore spills erases their actual impact as cumulative environmental hazards. The regularity of ‘minor’ oil spills, I argue, forms a category of chronic disasters obscured by an ‘emergency frame’ that defines ecological catastrophes as acute, traumatic and exceptional. Unlike eruptive and explosive spills, Husky visualized the SeaRose spill as a benign event, drawing attention away from the ongoing and incremental nature of oil pollution. In this way, Husky’s representation of the spill produced more public relief than alarm.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".