Engaging youth in research planning, design and execution: Practical recommendations for researchers
Bibliographic record
Abstract
CONTEXT: Engaging youth as partners in academic research projects offers many benefits for the youth and the research team. However, it is not always clear to researchers how to engage youth effectively to optimize the experience and maximize the impact. OBJECTIVE: This article provides practical recommendations to help researchers engage youth in meaningful ways in academic research, from initial planning to project completion. These general recommendations can be applied to all types of research methodologies, from community action-based research to highly technical designs. RESULTS: Youth can and do provide valuable input into academic research projects when their contributions are authentically valued, their roles are clearly defined, communication is clear, and their needs are taken into account. Researchers should be aware of the risk of tokenizing the youth they engage and work proactively to take their feedback into account in a genuine way. Some adaptations to regular research procedures are recommended to improve the success of the youth engagement initiative. CONCLUSIONS: By following these guidelines, academic researchers can make youth engagement a key tenet of their youth-oriented research initiatives, increasing the feasibility, youth-friendliness and ecological validity of their work and ultimately improve the value and impact of the results their research produces.
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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 | 0.324 | 0.294 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier 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".