A Review of Approaches, Strategies and Ethical Considerations in Participatory Research With Children
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.040 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it