Moving Beyond Indignation: Stakeholder Tactics, Legal Tools and Community Benefits in Large-Scale Redevelopment Projects
Bibliographic record
Abstract
Government and accompanying business interests often favour large-scale urban projects to promote urban growth, attract revenues, and place the city on the world stage. Such projects are primarily oriented towards consumption and spectacle, serving regional, if not global, clientele. Negative impacts – from traffic to displacement – are felt most heavily in the immediately adjacent areas, and developments often contribute to increases in socio-spatial polarization. This paper examines two redevelopment projects, one in South San Francisco, one in Montréal, to assess the tactics and legal tools employed by municipal authorities and local organisations to harness development for social and environmental ends. Associated legal tools include public consultation requirements, citizen ballot propositions, Community Benefits Agreements and Development Agreements. The paper concludes with recommended principles to underpin future development and cautionary notes about the limitations of these tools. Los gobiernos e intereses empresariales que los acompañan, favorecen a menudo proyectos urbanísticos de gran escala, para promover el crecimiento urbano, atraer ingresos, y poner la ciudad en el mapa. Estos proyectos están orientados principalmente hacia el consumo y el espectáculo, al servicio de una clientela regional, si no global. Los impactos negativos –desde el tráfico a los desplazamientos– se dejan sentir con más fuerza en las áreas inmediatamente adyacentes, y su desarrollo a menudo contribuye al aumento de la polarización socio-espacial. Este artículo examina dos proyectos de reurbanización, uno en el sur de San Francisco, y el otro en Montreal, para evaluar las tácticas y herramientas legales empleadas por las autoridades municipales y organizaciones locales para potenciar el desarrollo de los fines sociales y ambientales. Entre las herramientas jurídicas asociadas se incluyen los requisitos de consulta pública, propuestas electorales ciudadanas, acuerdos sobre beneficios a la comunidad y acuerdos sobre el desarrollo. El artículo concluye con recomendaciones para sustentar el desarrollo futuro y una nota de advertencia sobre las limitaciones de estas herramientas. DOWNLOAD THIS PAPER FROM SSRN: http://ssrn.com/abstract=2562886
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".