Re-imagining Crowsnest Pass: Findings ways of redeveloping/reskilling a coal mining community
Bibliographic record
Abstract
hile world leaders gathered in Glasgow to decide strategies to face climate change at the 2021 COP26 conference, small communities around the world, dependent on current energy practices, struggle to come to terms and adapt to the phaseout of traditional energy sources. The Municipality of Crowsnest Pass in the Canadian Rockies is a coal mining community needing to reimagine its future to redevelop and reskill a dwindling economy. Through a community-based participatory research collaboration between citizens and students, using mixed methods of spatial analysis and morphology, public participatory processes and experiential learning, a series of redevelopment and reskilling strategies were drafted. Through a comprehensive analytical approach to find future potential, many opportunities arose, building confidence in existing assets and generating new ideas for change. Design ideas were drafted based on a rich cultural and natural landscape. Different scenarios and strategies for investment and redevelopment could drive fundraising efforts at the local, provincial and federal levels. Those ideas were very well received by the community. Students and community members became a great partnership: respectful, enthusiastic and empathetic. While implementation will be difficult, the redevelopment strategies and the processes itself presents a renewed impetus for change with an optimistic view for the future for Crowsnest Pass.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.030 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.007 |
| 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".