Summary ReseaRching the URban Dilemma: URbanization
Bibliographic record
Abstract
The following summary highlights the key findings of the baseline study Researching the Urban Dilemma: Urbanization, Poverty and Violence. The study’s goal was to review the state of evidence and theory on the connection between urban violence and poverty reduction, and on the impact and effectiveness of different interventions. The study finds that there is considerable engagement with issues of urbanization, urban poverty and urban violence by social scientists. Much is known on the scale and distribution of urban growth, as well as on the character of urban impoverishment and inequality. There is also considerable research being conducted on the real and perceived costs and consequences of urban violence across an array of low- and medium-income settings. However, the study also reveals that much of the research and debate continues to be segmented and compartmentalized within certain disciplines and geographic settings, and that there are major silences in relation to the interaction between urban poverty and urban violence. The summary highlights a sample of interventions designed to prevent and reduce urban violence, but notes that the effectiveness of many interventions designed to mitigate and reduce insecurity and poverty in medium- and lower-income cities has yet to be tested. The summary concludes with a review of key knowledge gaps and questions for future research. i n ternat ional development re search centre About Safe and Inclusive Cities Safe and Inclusive Cities is a jointly-funded research initiative aimed at building an evidence base on the connections between urban violence, poverty and inequalities. It also seeks to identify the most effective strategies for addressing these challenges. Safe and Inclusive Cities is managed by Canada’s International
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".