Rethinking resort growth: understanding evolving governance strategies in Whistler, British Columbia
Bibliographic record
Abstract
This paper examines shifts in governance and management strategies that have occurred in response to endogenous and exogenous pressures on the mountain resort of Whistler, British Columbia. Since its inception in the mid-1970s, Whistler has pursued successive innovative management approaches that have emphasized growth. The most recent approach, integrated comprehensive sustainability planning, reflects a response to reaching the planned limits of resort development and suggests the emergence of a new “corporatist” governance model based on principles of sustainability. However, the complex effects associated with exogenous factors, such as the global economic crisis, hosting the Winter Olympic Games and the increasing political necessity of collaboration with local First Nations (indigenous peoples), raise questions concerning the degree to which Whistler is “locked-in” to the pro-growth model of governance. A path dependency framework is employed to explore and explain Whistler's evolving forms of governance. While briefly reviewing the earlier pro-growth path of Whistler's development, particular attention is paid to factors underlying the implementation and continuing challenges of the comprehensive sustainability governance model. Other issues explored include the viability of no-growth governance, the issues surrounding growth limits and the role of “The Natural Step” framework in tourism governance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".