Creating Space for Youth Voice: Implications of Youth Disclosure Experiences for Youth-Centered Research
Bibliographic record
Abstract
This paper examines youth’s disclosure experiences within the context of chronic illness, drawing on examples from IN•GAUGE, an on-going research program led by Dr. Roberta L. Woodgate. Youth’s descriptions of their disclosure experiences provide valuable insights into the ways in which they use their voice in everyday life. This examination of the disclosure experiences of youth offers a lens through which the concept of youth voice in the research process can be understood and youth’s agency foregrounded. We present implications for researchers, ethics boards, funding agencies, and others who engage in youth-centered research, and offer alternative terminology to use in characterizing the elicitation and dissemination of youth voice in the research process. We contend that conceptualizing such efforts as giving youth voice has the potential to discredit the significant agency and autonomy that youth demonstrate in sharing their stories, perspectives, and opinions within the research context. We advocate for the adoption of the phrase of providing or creating space for youth voice, as one alternative to the phrase giving youth voice
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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 | 0.054 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".