Extending Youth Voices in a Participatory Thematic Analysis Approach
Bibliographic record
Abstract
Recent decades have seen a more thoughtful discussion regarding the inclusion of children and youth in research and decision making, challenging how we conduct child and youth-focused studies. Included is a focus on Youth Participatory Action Research approaches and how they facilitate engagement of child and youth voice. Similarly, there is a smaller yet equally important questioning of how we understand “voice,” drawing attention to the conceptualization of “voice,” and the need to account for its social positioning and construction. Despite these various advances, current discussions focus predominantly on research design and data gathering, with an emerging focus on the dissemination of findings. Discussions focused specifically on data analysis remain limited. This omission seems important, given the bridge analysis forms between data gathering and dissemination of findings, and how this impacts youth engagement in the research process overall. By not considering more thoughtfully the ways in which children do or do not engage in the analysis of their data, how are we impacting the positioning of their “voice” in the findings? Similarly, how does our analysis unintentionally strengthen or undermine the platform from which youth share their findings, especially with those in positions of power? In response to these questions, we use this article to consider data analysis in relation to voice and subsequent knowledge production. We also share our approach to participatory thematic analysis in the Spaces & Places research project, a participatory action research program with Indigenous youth in three communities of Atlantic Canada. Through the discussion and exemplar, we hope to contribute to how researchers consider “voice,” ours and those of child and youth collaborators, and the ways in which we can account for both in the analysis process, and enhance the voices of children and youth as knowledge brokers in the dissemination that follows.
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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 | 0.231 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.003 | 0.005 |
| 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".