The short and long of collaborative planning in the mountain resort destination of Canmore, Canada
Bibliographic record
Abstract
This paper examines the outcomes of a collaborative planning exercise in the mountain town destination of Canmore, Canada, and offers a unique view of the experiences and challenges facing community leaders, planners and residents in post-process strategy implementation and monitoring. Diverse resident and stakeholder interests came together in 1994 in a year-long collaboration to develop a Growth Management Strategy (GMS). While short-term process outcomes are reported elsewhere, this paper focuses primarily on the medium- to longer-term outcomes and the experiences of participants eight–nine years after the GMS process ended. Tangible and intangible, as well as direct and indirect outcomes were identified. The results show that some gains appear to have faded overtime, significant challenges were experienced in strategy implementation and monitoring, while growth continued to impact the social–cultural fabric of the community. However, a number of positive gains and efforts indicate that collaborative processes complemented by citizen-based strategic visioning and strategic planning can be valuable for direction setting, growth management and sustainable community development in destinations like Canmore. Implications for research and practice are discussed. The study also suggests the need for a new planning paradigm to manage such complex community domains.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".