Draw-Write-Narrate method brings forward diverse student voices in research on gender violence in Kenyan primary schools
Bibliographic record
Abstract
Using open-ended art-based interviews with the Draw-Write-Narrate method (Ogina and Nieuwenhuis, 2010) to investigate gender violence in primary schools in Kirinyaga County, Kenya resulted in many benefits and challenges related to power, perspective, protection and credibility. This presentation describes the methodology and experience of conducting arts-based methods with children and lessons learned from meaning-making amidst a plethora of student narratives. Following three months of participant observation, individual semi-structured interviews were conducted with teachers and individual interviews were conducted with male and female upper primary school students in two case study schools. The art-based approach to student interviews enabled a student-centered process that was child-friendly and encouraging while minimizing risks to participants. Art-based research with children requires researchers to approach data collection and analysis with caution and reflexivity in order to highlight participants
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".