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Record W3134205750 · doi:10.54656/wtlp5633

The Role of Scientific Evidence in Canada's West Coast Energy Conflicts

2021· article· en· W3134205750 on OpenAlexaboutno aff
Holly Clermont, Ann Dale, Leslie A. King, Maureen G. Reed

Bibliographic record

VenueJournal of Community Engagement and Scholarship · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsSalience (neuroscience)CredibilityEnvironmental resource managementClimate changePolitical scienceGeographyEnvironmental planningEcologyPsychologyEnvironmental science

Abstract

fetched live from OpenAlex

With salience, credibility, and legitimacy as organizing themes, we investigated how opposing communities engaged with scientific information for two contentious proposed energy projects in western Canada, and how their perceptions of science influenced its use in decision-making. The Trans Mountain pipeline expansion, to carry diluted bitumen from northern Alberta’s oil sands to tankers on British Columbia’s (BC) south coast, was expected to adversely impact biodiversity and contribute to climate change. The Bute Inlet hydroelectric project, a large renewable energy project planned for BC’s Central Coast, was anticipated to impact biodiversity but was largely seen as climate-friendly. Based on surveys and interviews with 68 participants who had made one or more personal or professional decisions pertaining to the projects, we discovered that values, cultural cognition, and media effects permeated all aspects of using scientific evidence—from commissioning scientific research to selecting, assessing, and weighing it with other forms of information. As a result, science was developed and used to support positions rather than to inform decisions. We discuss ways to improve the use of science in environmental assessments and other planning and development processes where engaged communities are divided by oppositional positions. We hope this research will lead to community-university partnerships that identify broadly salient, credible, and legitimate sources of information about energy and climate issues, and foster knowledge mobilization across conflict divides.

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)0.043
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.779

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0430.097
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0110.011
Science and technology studies0.0460.042
Scholarly communication0.0300.006
Open science0.0030.020
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.106
GPT teacher head0.314
Teacher spread0.208 · 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 designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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