CENTERING GRASSROOTS ACTORS IN NETWORKING FOR CHILD PROTECTION IN EAST AFRICA
Bibliographic record
Abstract
Violence against children (VAC) is both a global and local concern that has resulted in several child protection initiatives by formal and informal networks in East Africa. The dominant narrative on networking for VAC prevention and response significantly focuses on the functionality of formal networks and ignores grassroots networks. We conducted research to explore the functionality and corresponding impact of diverse networks that work to prevent and respond to VAC in Kenya, Tanzania, and Uganda. Study participants were VAC network leads at grassroots, subnational, and national levels, and network funders. Data were collected using interviews, document review, and focus group discussions. We found that scholarly literature illuminates the role of formal networks at the expense of grassroots networks, which are ignored and minoritized in literature. This may contribute to a disparity between the funding of grassroots and formal networks. Yet, grassroots network actors are VAC first responders and are instrumental in child protection work. We contend that it is vital to center grassroots networks in VAC policies, programs, and research in order to achieve sustainable connections between networks, communities, and funders, and to empower communities to protect children from abuse.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".