Security, Resilience and Participatory Urban Upgrading in Latin America and the Caribbean
Bibliographic record
Abstract
ABSTRACT In theory, security and resilience in contexts of violence and crime are improved by participatory urban upgrading. Yet, upgrading practices actually demonstrate how vulnerabilities to violence, insecurity and crime are reproduced by state–society and intra‐community power hierarchies. On the one hand, the priorities and perspectives of politicians and bureaucrats continue to take precedence over the needs and demands of residents of marginalized communities, undermining participation. On the other hand, the internal socio‐political structures of marginalized communities complicate the capacity and willingness of residents and external state actors to engage with each other. The result is that upgrading programmes are not particularly successful in ordering development and security or in creating resilience. Internal processes have a greater impact on residents’ choices in their daily struggles to survive and thrive, but the resilience they create is limited because power and resources tend to be centralized and sometimes linked to crime groups. This article uses the cases of Kingston (Jamaica) and São Paulo (Brazil) to highlight these power hierarchies and how they impede the resilience project of participatory urban upgrading processes in contexts of crime and violence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".