Rage Against the Growth Machine: Investigating Urban Growth Machine Fragility and Citizen Resistence to Major Development Projects in Berlin and Montreal
Bibliographic record
Abstract
Through the analysis of three cases, the attempted development of a former airport in Berlin and a casino mega project and large condominium development on former industrial lands in Montreal, this study examines the possibility of increasing urban growth coalition fragility and the implications for urban growth machine theory. Since its first articulation by Harvey Molotch in 1976, the “city as a growth machine” has been a foundational idea of urban governance and urban political economy. A new wave of scholarship has suggested that urban growth coalitions, groups of place-bound elites with an interest in land use intensification who tend to dominate local political processes may be growing increasingly fragile. The cases examined in this study, two of growth coalition failure, and one of major concessions instead reflect a more nuanced idea that powerful counter-coalitions and contextual factors allowed community groups to prevent development. In both cities well-organized oppositions, with histories of activism, invoking ideas around the “right to the city”, operating in contexts with strong municipal political party systems, seemed to be more important factors than the fragility of individual growth-coalitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".