What Compels People to Take Action for Nature: A Case Study of the Crowsnest Pass Conditions, Strategies, and Lessons for Conservation
Bibliographic record
Abstract
In May 2020, the Alberta government abruptly rescinded a long-standing coal policy, quietly opening 53,000 km² of the Eastern Slopes, critical headwaters and essential habitat within North America's Yellowstone to Yukon wildlife corridor, to open-pit coal mining. In response, a diverse grassroots movement emerged in the Crowsnest Pass region, bringing together ranchers, Indigenous groups, conservationists, artists, and local leaders around a shared commitment: to defend a landscape central to their identities, livelihoods, and communities. This qualitative case study explores the conditions, strategies, and lessons from the Crowsnest Pass Coalition, addressing the central question: What compels people to take action for nature? Through an inductive research approach incorporating a comprehensive literature review, analysis of media coverage, and twelve semi-structured interviews conducted in Alberta, I identify emotional (e.g., place attachment, identity, feelings of betrayal), cognitive (e.g., moral responsibility, justice), and functional motivations (e.g., threats to water security, health, and local livelihoods) as critical factors driving coalition members' actions. The government's sudden policy shift acted as a catalyst, transforming environmental concern into widespread civic action. Strategically, the coalition succeeded by coming together around their universally valued concerns for water security, bridging diverse groups across traditional political divides. Their decentralized, facilitative ("soft") leadership approach allowed autonomy, encouraged trust, and mitigated activist burnout. Additionally, public figures and sustained investigative journalism were vital in maintaining pressure, mobilizing new supporters, and countering government narratives. The coalition encountered significant challenges, including social backlash, personal and professional risks, and the ongoing nature of conservation work. The study's findings underscore that effective conservation advocacy is profoundly relational and deeply rooted in emotional and community-driven concerns. The success of the Crowsnest Pass Coalition highlights the necessity of long-term adaptive strategies, meaningful stakeholder engagement, and sustained momentum. This research contributes valuable insights for this very conservation group continuing their efforts and also others beyond the Crowsnest Pass aiming to understand and harness place-based motivations to build more resilient, inclusive, and enduring conservation movements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".