An arts-based, peer-mediated Story Board Narrative Method in research on identity, belonging and future aspirations of forced migrant youth
Bibliographic record
Abstract
An innovative, arts-based, peer-mediated Story Board Narrative method of data collection in an ongoing, multi-sited Youth Migration Project is described. The research explores negotiated identity, belonging and future aspirations of forced migrants aged 11 to 17 years old living temporarily in Thailand and Malaysia. The unique data collection method centres meaning making by youth about their forced migration and adaptation in often hostile and precarious conditions. Primary data are youths’ narrative accounts of an arts-based Story Board that each youth creates over a four week period and then presents to a small group of migrant peers. Follow-up sessions invite youth to revise their Story-Board and their narrative, with inquiry led by peers rather than research facilitators. The method positions youth as experts and in control of their own stories. Story Board Narratives are audio-taped, transcribed, and content analyzed by a team of investigators who also have migration experiences. Unlike other visual methods that prescribe drawings and focus on the visual production, this method allows youth to direct their own visual representations and the narrative associated with them. The method enables a developmental process whereby youths’ introspection, discussions, and representations of the impacts of forced migration evolve over time. This emergent, participatory, arts-based method as the centerpiece in a mixed method research design yields richly nuanced and often unexpected findings that may not have been generated through methods that are more prescriptive, structured, investigator-centered, and deductive.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".