Reducing urban violence in the global South towards safe and inclusive cities
Bibliographic record
Abstract
Reducing Urban Violence in the Global South seeks to identify the drivers of urban violence in the cities of the Global South and how they relate to and interact with poverty and inequalities. Drawing on the findings of an ambitious 5-year, 15-project research programme supported by Canada's International Development Research Centre and the UK's Department for International Development, the book explores what works, and what doesn't, to prevent and reduce violence in urban centres. Cities in developing countries are often seen as key drivers of economic growth, but they are often also the sites of extreme violence, poverty, and inequality. The research in this book was developed and conducted by researchers from the Global South, who work and live in the countries studied; itchallenges many of the assumptions from the Global Northabout how poverty, violence, and inequalities interact in urban spaces. In so doing, the book demonstrates that accepted understandings of the causes of and solutions to urban violence developed in the Global North should not be imported into the Global South without careful consideration of local dynamics and contexts. Reducing Urban Violence in the Global South concludes by considering the broader implications for policy and practice, offering recommendations for improving interventions to make cities safer and more inclusive. The fresh perspectives and insights offered by this book will be useful to scholars and students of development and urban violence, as well as to practitioners and policymakers working on urban violence reduction programmes
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".