Children’s participation in social work research. A secondary data analysis of an umbrella review of children’s participation in child welfare
Bibliographic record
Abstract
Abstract The real participation of children and young people (C&YP) in decisions affecting their lives and rights gained traction with the 1989 adoption of the UN Convention on the Rights of the Child, particularly Article 12, which asserts children's rights to express their views and have them respected. This principle recognizes children as active societal agents, not just passive subjects. This study, based on these principles, analyses secondary data from a prior umbrella review by the authors, focusing on children’s involvement in research processes. Using an adapted version of Shier's (2019) model, the study highlights a worrying dependence on traditional, adult-centred research methods that largely overlook young participants' perspectives. Although some creative, child-friendly methods are used, they are sporadic, revealing a gap in engaging younger children effectively. The study also finds that children's participation is often tokenistic rather than genuinely collaborative. These findings emphasize the need for more inclusive and innovative research practices to empower C&YP as co-creators of knowledge. Bridging these gaps is essential for promoting a more child-centred, equitable approach in research, which values every child's contribution and fosters a more inclusive society.
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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 | 0.061 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".