Drawing Out Their Stories: A Scoping Review of Participatory Visual Research Methods With Newcomer Children
Bibliographic record
Abstract
Researchers are increasingly using participatory visual methods (PVM) to gain a deeper understanding of newcomer children’s experiences, sense of identity, relationships, needs, strengths, and aspirations. By taking photos, producing digital stories, creating maps, drawing, sculpting, and other visual-based practices, children can help us understand how they navigate their complex worlds. We conducted a scoping review to explore what is known about participatory visual research with newcomer children. We searched nine databases, screened 692 articles, and included 21 articles for synthesis and analysis. Five common and connected areas were identified as important for consideration when envisioning, planning, and conducting this type of research with newcomer children: PVM provides an opportunity for children to communicate complex feelings and disrupt deficit discourse; participation in PVM research is highly dependent on varying cultural, economic, and relational factors; providing a range and choice of data collection activities permits deeper engagement and higher quality data; PVM can enhance meaningful engagement, reduce power asymmetry, and engender confidence and self-awareness; developing and sustaining trusted relationships are integral to the research process. The review reveals the need for more researcher reflexivity with an explicit attention to assumptions, values, and ethical considerations and suggests opportunities for researchers to better ensure newcomer children can share and shape their own stories.
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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 | 0.061 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".