REFLECTIONS ON NETWORKING DYNAMICS TO ADDRESS VIOLENCE AGAINST CHILDREN IN TANZANIA
Bibliographic record
Abstract
This article is based on a study conducted to understand the functionality and connectivity of existing networks and their impact on the prevention and response to violence against children (VAC) in East Africa. We adopted an exploratory qualitative approach in which a bottom-up purposive selection of study participants was used. Data were collected using focus group discussions with grassroots actors, interviews with network leads at the grassroots district and national levels, and VAC network funders. The study was carried out in Tanzania’s Dar es Salaam region in three districts (Kigamboni, Temeke, and Ilala) and eight wards. Our findings show that because the nature of VAC is complex and multidimensional, efforts to respond to it also exhibit these qualities. Depending on the goal, networking takes various forms, and VAC networks can have unspecified lifespans. VAC networking results from strategic decision-making that yields many benefits, including a stronger voice and visibility, enhanced impact, and potential efficiency. However, networks also encounter bottlenecks that negatively impact their goals. This is an indication that VAC network actors ought to be more reflexive regarding the space they occupy in the network and intentionally pursue strong relationships among actors and networks.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".