Mask-Making and Drawing as Method: Arts-Based Approaches to Data Collection With War-Affected Children
Bibliographic record
Abstract
Globally, the numbers of children living in conflict zones and displaced by war have risen dramatically over the past two decades, and with this, scholarly attention to the impacts of war on children. More recently, researchers have examined how war-affected children are being studied, revealing important shortcomings. These limitations relate to the lack of child participation in research, the need for researchers to engage children in the research process as “active agents” rather than “passive objects” under study, as well as the need for researchers to pay closer attention to ethical dilemmas associated with researching war-affected children. To address these realities, innovative research methods that can be adapted across diverse sociocultural contexts are warranted. In light of these shortcomings, our research team integrated two arts-based methods: mask-making and drawing, alongside traditional qualitative data collection methods with a particularly marginalized population of young people: children born in captivity within the Lord’s Resistance Army in northern Uganda. In this article, we provide information on the context of northern Uganda. We describe how the use of mask-making and drawing was used as data gathering tools and the ways in which these arts-based methods had important benefits for the research participants, researchers, and impacted on the validity of the research as a whole. We propose that the use of these participatory visual methods enriched the themes elicited through more traditional methods. The article describes how these arts-based mediums fostered community building among children typically excluded from their communities and were successful as a tool to build trust between participants and the research team when exploring sensitive topics. The article concludes with implications for future research with war-affected children.
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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 | 0.118 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".