Breaking the Cycle: Women’s Perceptions of the Causes of Violence and Crime in Informal Settlements in Nairobi, Kenya, and Their Strategies for Response and Prevention
Bibliographic record
Abstract
Crime and violence are serious issues in informal settlements around the world. To date, there is a dearth of evidence about the causes of and effective strategies for reducing and preventing violence and crime in informal settlements in cities in the Global South. Additionally, women’s voices are often absent from research focused on violence and crime prevention and reduction in informal settlements. The purpose of this study, therefore, was (1) to identify potential causes of violence and crime in informal settlements, as perceived by women living in Mathare informal settlement in Nairobi, Kenya and (2) to highlight residents’ strategies for response and prevention. Fifty-five in-depth and walk-along interviews were conducted with women living in Mathare in 2015-2016. A modified grounded theory approach was used to guide data collection and analysis. The most common contributor to violence and crime identified by women in Mathare was idle youth, but leadership and government challenges, corruption and/or inadequacy of police, community barriers, tribalism, and lack of protective infrastructure also emerged as contributing factors. Despite facing many economic, environmental, and day-to-day challenges, women in Mathare identified violence and crime as predominant issues; thus, developing effective response and prevention strategies to these issues is paramount. Women discussed many strategies and initiatives to reduce and prevent violence and crime in informal settlements, but also identified barriers to implementing them. Findings suggest there is a need for trust-building between formal and informal sectors of the community, systems of accountability, and long-term investment to foster sustainable and effective violence and crime response and prevention in these settlements.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".