Youth knowledge mobilization: Reflections on theory and practice
Bibliographic record
Abstract
This article explores knowledge mobilisation in youth research. We take as a starting point that youth knowledge mobilisation (YKMb) requires specific strategies because of the unique power dynamics involved in mobilising knowledge related to young people. However, existing knowledge mobilisation models cannot account for these specificities. YKMb often requires co-creation with partner organisations as well as with youth themselves, leading to diverse and sometimes fragmented approaches to YKMb. An overarching discussion about the theory and practice of YKMb is missing from the literature. To explore the factors that influence the diversity of approaches to YKMb, we take up reflexivity to explore the experiences of a YKMb Chair working in intersectoral partnerships as well as with young people in Quebec, Canada. This article features an emergent YKMb framework that conceptualises a continuum of approaches to mobilising knowledge about, for, with and by youth. Across these modes of working, several factors influence YKMb in practice, from research paradigm and context as well as specificities regarding which actors are involved and why these different actors want to mobilise knowledge, as well as what roles different actors play in knowledge production and mobilisation. These factors influence the continuum of roles that academic researchers may play in YKMb, from more traditional roles as knowledge translators to engaged roles such as facilitators, advocates and learners. Conceptualising YKMb through continuums of practice offers critical insights to support intersectoral and interdisciplinary teams of academic researchers, partners and young people in research co-creation to better bridge the gap between research and practice.
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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 | 0.055 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.026 | 0.074 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.007 | 0.011 |
| 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".