A Review of Approaches, Strategies and Ethical Considerations in Participatory Research With Children
Bibliographic record
Abstract
Participatory research can change the view of children from research subjects to active partners. As active partners, children can be recognized as agents who can contribute to different steps of the research process. However, “participatory research” is an umbrella term that covers both the collection of data with children and children’s participation in making decisions related to the research process. As such, it raises particular challenges for researchers. Based on a pragmatic ethics approach, we were inspired by the realist review methodology to synthesize the current literature, identify different strategies used to engage children aged 12 and below in participatory research, and analyze how they affect children’s active participation and the ethical aspects related to each. Fifty-seven articles were retained for inclusion in the review. A variety of strategies were used to involve children in the research process, including discussion groups, training/capacity-building sessions, photography and filming, children as data collectors and questionnaires. The most prevalent ethical considerations identified were related to power dynamics and strategies to facilitate children’s expression and foster the authenticity of children’s voices. Researchers should address these ethical considerations to actively involve children within the research process and prevent tokenistic participation. Active inclusion of children in research could include co-identifying with them how they want to be involved in knowledge production (if they want to) from the beginning of a project.
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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 | 0.053 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".