From Growth Machine to Growth Management: The Dynamics of Resort Development in Whistler, British Columbia
Bibliographic record
Abstract
In North America, competition for land has often been conceptualized as being driven by growth machines whereby those with common stakes in development form coalitions of local elites to influence government in pursuit of their goals. The inequitable benefits of growth have been challenged more recently by the introduction of growth-management practices that heighten the role of local residents in land-use decisions. In this paper, the concepts of the ‘growth machine’ and ‘growth management’ are applied to an examination of the resort community of Whistler, British Columbia. This approach transforms previous theorizations of resort formation which draw upon Butler's (1980) life-cycle model, by focusing on the social and political dynamics of growth. Whistler is seen to progress through a phase of uncontested growth-machine dominance, to a phase of local contestation that is then moderated by the introduction of growth-management practices. The evolutionary process is seen as a cumulative one in which, over time, social and environmental imperatives are imposed upon the economic imperatives of the growth machine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".