Ecological Modernization or Sustainable Development? Vancouver's<i>Greenest City Action Plan</i>: The City as ‘manager’ of Ecological Restructuring
Bibliographic record
Abstract
A framework for assessing cities as contributors to sustainable development (SD) is proposed. Differentiating between SD and ecological modernization (EM), we contend that even the weakest EM reforms prompt ecological restructuring (ER). Once unleashed, ER creates four problematics—ecological, economic, political and cultural—which governments at different scales must address by maintaining or challenging the status quo in relation to global structural imperatives; notably, requirements to promote economic growth and maintain legitimacy. That is, ER fosters uneven, non-linear processes of societal learning, which apply at different scales. Hence, where national governments lag behind, cities that develop ‘action plans’ may prompt SD. The framework is road tested by evaluating Vancouver's Greenest City Action Plan. We find that Vancouver does indeed push ER towards SD, especially in the political and cultural domains, even as Canada and British Columbia appear less committed. However, due to the proximity of city government to citizens' lived experiences of unsustainable development, Vancouver, like other cities, confronts a distinct kind of democratizing pressure. A future research agenda on this issue would aim to uncover how planning for SD must both foster and manage this democratizing pressure, which arises as informal (spontaneous, ‘from below’) participation meets formal (procedural, ‘top-down’) process.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".