‘Scientists don’t care about truth anymore’: the climate crisis and rejection of science in Canada’s oil country
Bibliographic record
Abstract
Recent research in the area of science and technology studies focuses on climate change denial, the spread of misinformation, and public distrust in climate scientists; these beliefs are held especially by those dependent on fossil fuel extraction for their livelihoods. Many of the same individuals who deny the scientific consensus on climate change are nevertheless directly impacted by the climate crisis and environmental disasters. In fossil fuel dependent locations, do people continue to deny the scientific consensus on climate change and distrust climate scientists even after themselves experiencing a catastrophic flood? This paper investigates this question through interviews with 40 people affected by the 2013 Southern Alberta Flood, the costliest flood in Canadian history, who also live in the City of Calgary, the economic hub for Canada’s tar sands. Results indicate the participants rejected the scientific consensus on climate change, voiced a distrust in the motivations of climate scientists, though hoped they would one day discover the ‘truth’, and worked discursively to protect the oil industry. The findings reveal the complexity of post-disaster environmental views and trust in science, as well as how fossil fuel dependence shapes these views.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.057 | 0.039 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".