The Alberta Oil Sands: Factors of Risk Perception and Outrage
Bibliographic record
Abstract
Every major project has some associated environmental, health and safety hazards and this is equally true for major energy projects. Environmental impact assessments, cost-benefit analysis and economic projections are tools used by regulators to determine the acceptability of hazards. Sometimes, however, the risks the public perceives to be associated with a given project are disproportionate to the actual hazards that exist. Peter Sandman uses the term outrage to characterize the verbal opposition, expressions of concern and political activism that occur as a result of inflated risk perception. Public outrage can create reputational challenges for projects, challenging their social licence to operate, delaying approval processes and slowing economic growth, despite regulatory approvals to ensure technical, health and safety. While much of the risk perception literature is applicable to the benefits of major projects, this paper will focus solely on the real and perceived environmental, health and safety costs. Any conversation around risk communication must acknowledge that public outrage can often lead to better project outcomes,, can be quite legitimate and is always important. Citizens have every right to be concerned and interested in any activity of both private and public undertaking that impacts their environment, health or social wellbeing. Public opinion is a crucial check and balance to industrial profit maximizing and corporate interests. However, the correlation between actual hazard and public outrage is remarkably weak. If a list of hazards is rank-ordered by "expected annual mortality ... and then rank-ordered (again) by how upsetting the various risks are to people, the correlation between the two rank-orders would be approximately 0.2". Such a weak correlation between actual hazard and public outrage makes it possible to manipulate public outrage, amplifying or attenuating it to suit a certain preference. Disproportionate risk perceptions confound rational, responsible decision making and challenge the development of good public policy. Assuming that "the oil sands have a reputational crisis not an environmental one", why do the oil sands provoke such outrage?3 What can be done to subdue public concern to a level that more appropriately befits the hazard level in order to facilitate improved policy discussions?
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".