“There’s Always Winners and Losers”: Traditional Masculinity, Resource Dependence, and Post-Disaster Environmental Complacency
Bibliographic record
Abstract
The 2013 Southern Alberta flood was a costly and devastating event. The literature suggests that such disasters have the potential to spur greater environmentalism and environmental action, as residents make connections between global environmental change and local events. However, the literature also suggests that residents in communities dependent on fossil fuel extraction might see technological disasters, like oil spills, as threats to their economic well-being, thereby limiting environmental reflexivity. Given that Alberta is home of the tar sands, how might a flood disaster affect men’s environmental views, given both traditional notions of masculinity and men’s economic dependence on oil production? Using a survey of 407 flood-affected residents of Calgary and in-depth qualitative interviews with 20 men directly impacted by the flood, this article demonstrates men’s decreased tendency to change their environmental views after the flood. The qualitative data reveal that men justify this reluctance by shifting blame for climate change to the Global South, by arguing for the economic centrality of the tar sands for Alberta, and by discussing how a warming climate will largely be a positive outcome for Alberta. The article concludes with discussion of relevance for environmental sociology and for public policy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".