LOCATING STATE ACTORS IN VIOLENCE AGAINST CHILDREN (VAC) NETWORKS IN KENYA: A COMPLEXITY LEADERSHIP LENS
Bibliographic record
Abstract
Kenya has made significant efforts to address violence against children (VAC), but its prevalence remains high. Networking of different actors has shown evidence of benefit in some sectors, but determining its effectiveness in addressing VAC has not received due scholarly attention. We conducted qualitative research, including a desk review, focus group discussions, and interviews. In this article, we apply complexity leadership theory to illuminate the types of networks involved and the influence Kenya’s government actors can exert towards eliminating VAC. We found that these actors operate through structured and unstructured networks. The latter are mainly grassroots responders who work voluntarily. The complexity leadership theory postulates that leadership influence is exercised through key functions, which are reflected in the two types of networks. The political–administrative function in Kenya is shaped by law; we show how it transforms other networks via an adaptive function. An enabling function is executed through enforcing policy, monitoring, and other methods, while a dissemination function involves the translation of ideas into policy, such as the transformation of Childline Kenya, a grassroots organization, into the National Child Helpline. We conclude that government should strengthen child rights networking by building more technical and financial capacity for this role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".