Changing National Policy on Violence Affecting Children: An impact assessment of UNICEF and partners' multi-country study on the drivers of violence affecting children in Peru
Bibliographic record
Abstract
The Multi-Country Study aimed to increase understanding of what drives violence affecting children in four countries – Peru, Italy, Zimbabwe and Viet Nam - and how best to address it. The impact assessment was conducted by independent researchers at the University of Edinburgh using an outcomes framework approach (Morton, 2015a) and focused on Peru. The study used a practically-focused, multi-partner approach to generating evidence that was important for subsequent impact. The specific combination of research outputs, awareness-raising, capacity-building and knowledgebrokering activities, built on this partnership approach, and maximised impact. UNICEF took a knowledge brokerage role to connect people with the research and to ensure key actors were aware of and included in the study, its findings and possible actions. Richer connections \nbetween research and policy were developed and sustained. Being engaged closely with the study helped local actors to be clearer about the issues of violence in their country, and was seen as a useful way of forwarding the agenda to tackle violence. Partnership kept levels of awareness high during a change of government. The study filled an evidence gap, helping to shift discourse on violence and give it higher political priority. There is now more capacity in Peru for academics, government analysts and policy makers to work together to address this issue and to get the evidence they need to develop policy. The research improved access to high quality information on violence, which in turn contributed to legislative changes, will help to leverage funding and has informed programmes at the ministerial level. It has also improved coordination efforts at the national level regarding violence prevention and has influenced how other countries in the region approach violence issues. Study partners will continue to work on violence issues. Levels of violence against children may have begun to decrease in Peru since the start of the study, but the final impact of the study is not yet known. The Research Contribution Framework used in this study was adaptable and effective in a middle income country.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".