“If we must secure our communities, we must do it together”: Co-creation of crime prevention and security governance in Lagos, Nigeria
Bibliographic record
Abstract
Developing partnerships between the state, the police, and local communities to prevent crime is increasingly recognized as valuable by security researchers and actors. Yet their effectiveness is undermined by a range of factors, particularly socio-political and institutional. Utilizing qualitative investigation, this study evaluates the specific forms of politics andstrategies being used to confront insecurity in Lagos with the involvement of various actors. The study explores why co-production in Lagos exists and what can be done, and by whom, for co-creation to thrive in the city. What the Lagos experience teaches is that crime reduction through co-creation is more likely to emerge and endure. This is the case even in a political landscape, where police power is centralized around the presidency and an executive bureaucracy, especially if there is a viable socio-economic case, such as when crime rates are high and the police lack capacity and numerical strength to fight crime or, worse, when the state provides security to some groups but not to others. Despite the many challenges, findings show that co-creation of crime prevention exemplifies many successes. The limitations on co-creation opportunities are noteworthy and will require significant political and institutional support moving forward.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".