Online Intergenerational Participatory Research: Ingredients for Meaningful Relationships and Participation
Bibliographic record
Abstract
Over thirty years ago, children’s participation rights were recognized internationally with the adoption of the United Nations Convention on the Rights of the Child (UNCRC). Increased involvement of children and young people in lead, collaborative, and advisory roles in interdisciplinary research has challenged ‘traditional’ adult research practices in numerous ways. Co-production recognizes participants as experts and creators of knowledge, engages children and young people in decision-making, and addresses traditional adult-child hierarchies. #CovidUnder19 is a movement that aims to foster intergenerational partnerships between children, young people and adult members of the child rights community to develop evidence-based advocacy to uphold children’s rights throughout the pandemic, as well as in response and recovery. The COVID 4P Log smartphone app was designed to better understand ways practitioners and policymakers protect, provide, enable participation, and prevent harm in their practice. Children and young people aged 14 to 19 from countries around the world are involved as co-researchers and advisors in research design, data analysis, and knowledge exchange. This paper explores the experiences of #CovidUnder19 young people as researchers focusing on the data analysis and knowledge exchange phase and includes their reflections on meaningful intergenerational partnership in research. This includes the importance of relationships, embracing the ‘inner child’, and fostering meaningful participation in the research process. The paper concludes with recommendations for other researchers on how to work in partnership with children and young people meaningfully to strengthen the process and impact for researchers and children’s human rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".