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Record W2157133999 · doi:10.1177/1086026612436979

“Governments Have the Power”? Interpretations of Climate Change Responsibility and Solutions Among Canadian Environmentalists

2012· article· en· W2157133999 on OpenAlexaffabout
Mark C. J. Stoddart, David B. Tindall, Kelly Greenfield

Bibliographic record

VenueOrganization & Environment · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of British ColumbiaMemorial University of Newfoundland
Fundersnot available
KeywordsClimate changeGovernment (linguistics)AttributionMoral responsibilityPower (physics)SustainabilityPolitical sciencePublic relationsState (computer science)Corporate social responsibilityBusinessEnvironmental resource managementPsychologyEconomicsSocial psychologyLaw

Abstract

fetched live from OpenAlex

The authors examine environmentalists’ attribution of responsibility for addressing climate change and their beliefs about solutions to this problem. Their analysis is based on responses to open-ended questions completed by 1,227 members of nine different environmental organizations. For these environmental movement participants, the federal government is seen as most responsible for addressing climate change. Government leadership is necessary because it has the power to set regulations and lead corporations and citizens toward pro-environmental behavior. However, a substantial number of participants also assert that “individuals are the driving force” in dealing with climate change. In this framework, individuals can take responsibility either through making lifestyle changes, or through applying pressure to government and businesses as citizens and consumers. Corporations are interpreted as unwilling to change on their own but must be coerced into becoming more environmentally sustainable by a strong state.

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)0.009
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.730

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0370.030
Scholarly communication0.0120.004
Open science0.0020.007
Research integrity0.0040.005
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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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 designQualitative
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

Citations46
Published2012
Admission routes2
Has abstractyes

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