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Record W3009568496 · doi:10.1111/basr.12194

Beyond petroleum or bottom line profits only? An ethical analysis of BP and the Gulf oil spill

2020· article· en· W3009568496 on OpenAlexaff
Mark S. Schwartz

Bibliographic record

VenueBusiness and Society Review · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsYork University
Fundersnot available
KeywordsDeepwater horizonContext (archaeology)HarmPetroleumOil spillOffshore drillingPetroleum industrySubmarine pipelineBusinessOceanographyGeographyEngineeringPolitical scienceEnvironmental protectionLawGeologyArchaeology

Abstract

fetched live from OpenAlex

Abstract On April 20th, 2010, an incident was to take place 49 miles off the Louisiana coast at the Macondo Prospect location in the Gulf of Mexico that would potentially change the future of offshore oil drilling. On that day, 11 men would lose their lives when the 33,000 ton Deepwater Horizon rig, owned by Transocean but leased by BP PLC, exploded. As a result of the explosion, millions of barrels of oil would be released into the Gulf of Mexico, leading to widespread environmental harm and devastation to the shoreline communities. To better examine the underlying reasons for how such an event could take place in 2010, this paper will unfold as follows. First, we provide context to the oil spill by discussing the history of BP, including its transformation into an “environmental” firm in 1995. Second, we explore and analyze the catastrophe through the lens of ethics. Finally, we analyze the disaster through a comparison with the U.S. 2008 financial crisis with a view to identify the root causes of the disaster and the string of ethical failures that have scarred the market economy over the past three or four decades.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.265
Teacher spread0.228 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2020
Admission routes1
Has abstractyes

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