Co-Creating Space for Voice
Bibliographic record
Abstract
This chapter explores the conditions that enable war-affected young people to assert and articulate their voices within the context of participatory research. While Quebec and Canada have seen an increase in war-induced migration of children and families, limited attention has been paid to war-affected young people’s active participation in research. Yet the contributions of young people in the research process are considered essential to providing more responsive services and programs. In this chapter, we take a critical look at how to engage war-affected young people in ways that can be empowering, transformational, and knowledge generating. Based on a collaborative inquiry with war-affected youth who participated in a 2-year youth forum alongside a multidisciplinary research team, we critically reflect back on the process to understand the challenges and opportunities in relation to participatory methodologies. Dominant themes that we pay attention to center around the roles and ways of creating space for: (a) the (un)structuring of the youth forum; (b) the need to navigate ethical issues, including power and privilege and the complex roles of researchers as both insiders and outsiders; and (c) the value of trust, relationship building, and art as a form of expression and change. The chapter, co-authored by both youth and adult researchers, will contribute to understandings of the considerations in ensuring that war-affected youths’ participation on research teams be empowering (and not disempowering).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".