The influence of environmental attitudes and behaviour in encouraging public acceptance of protestor violence towards the oil and gas sector in Canada
Bibliographic record
Abstract
Purpose This study aims to address the gap in current knowledge on the social acceptance of political violence against, or in response to, the Canadian oil and gas industry. Specifically, this research sought to determine if environmental attitudes and behaviours can be predictors for support of various degrees of violent pro-environmental protest. Design/methodology/approach Drawing upon Ajzen’s theory of planned behaviour and Oreg and Katz-Gerro’s model for predicting pro-environmental behaviour, the study examines data from a survey of 409 Canadians and uses step-wise regression to measure the association of predictors linking environmental attitudes with support for protester violence. Findings Findings suggest that personal willingness to sacrifice for the environment and a perception of environmental threat and concern are primary predictors linking environmental attitudes with support for protester violence. The study also identifies contextual factors such as age, activism history and police response tactics as influential. Practical implications The research contributes to understanding the complexities of environmental conflict and its implications for energy security policy. The results suggest that policies which encourage environmental sensitivity and commitment may be encouraging greater levels of activism and potentially violence against oil and gas companies. Originality/value While there exists research on the level of acceptance behind modern political violence in general, particularly against government in a broad sense, there is a noticeable absence of available literature on the risks of such political violence as it pertains specifically to oil and gas development and infrastructure in Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".