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Record W3082025407 · doi:10.1386/jem_00017_1

Death by a thousand spills: How corporate branding and media strategies downplay the risk of offshore oil spills in Canada

2020· article· en· W3082025407 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Environmental Media · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsOil spillEmergency responseSubmarine pipelineEnvironmental disasterEnvironmental scienceEngineeringEnvironmental protectionGeotechnical engineering

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.173
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it