Growth-Management Implementation in Metropolitan Vancouver: Lessons from Actor-Network Theory
Bibliographic record
Abstract
A case study is used to analyse metropolitan growth management implementation in Greater Vancouver, adding to a growing base of literature studying plan development and implementation through an actor-network theory (ANT) lens. It focuses on Metrotown, an office node initially designated in the Livable Region Plan and remaining regionally significant today. Unfortunately, Metrotown lost some momentum as business parks have seen more office growth in recent years. ANT's qualitative approach to inquiry is used to understand how and why this occurred. In ANT, an actor network emerges in response to any social goal, and is comprised of individuals, organisations, and inanimate artefacts including technologies, processes, laws, buildings, and infrastructure. In this case, the analysis emphasised how network fluctuation impacted plan implementation, including efforts to stabilise and destabilise relationships through what Latour calls black boxes of varying types. It also examined both successful and unsuccessful enrolment strategies. The case suggests that regional and municipal actors possessed enrolment skills but were unable to make more use of them. Further case studies are recommended to enhance planners' skills in coping with fluctuations and developing more effective enrolment strategies for implementation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".