NETWORKING FOR VIOLENCE AGAINST CHILDREN: AN ANALYSIS OF THE FORMAL–INFORMAL NETWORK DICHOTOMY IN UGANDA
Bibliographic record
Abstract
The rising cases of violence against children (VAC) have prompted strategies, laws, and policies to protect Ugandan children at the grassroots, subnational, and national levels. Despite the emergence of various strategies by different VAC actors who network formally and informally to address VAC, the functionality of their networks has not received adequate attention in previous research. We conducted a qualitative study to examine the dynamics of networking for VAC in Uganda. We collected data using interviews with network funders and leads at the national and subnational levels and focus group discussions with grassroots and community members. Our findings reveal that VAC networks belong to two broad categories: formal and informal. These exist side by side, usually operating in parallel, but sometimes with crisscrossing and overlapping activities. While the work of formal network actors is better resourced and recognized, and more visible, informal network actors are invisibilized in government plans, philanthropic efforts, and scholarly research. A more collaborative and inclusive VAC networking system would be instrumental in enhancing VAC prevention and response.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".