Child-Led Research with Young Children: Challenging the Ways to Do Research
Bibliographic record
Abstract
Child-led research is gaining increasing attention. Such research involves children leading throughout the research process, from research design to dissemination. Child-led research has tested adult-centric research assumptions, with debates in the literature about researchers’ expertise and responsibilities. If these debates are testing for child-led research undertaken with older children and young people, they are even more so for young children below school-starting age. This article examines child-led research undertaken in a Froebelian early years setting, over 11 months, with 36 children aged between 2 and 5 years, from the adult facilitators’ perspectives. The article utilises the research’s documentation, including mind maps, photographs and story books, songs and video recordings, and an interview undertaken with the facilitating early years practitioner and supporting academic. Learning from this, the article challenges the assumption, in much of the literature on child-led research, that adults need to transmit their knowledge of research methods to children. Instead, a ‘slow pedagogy’ can build on children’s own knowledge, collectively, with time to come to research understandings. The article concludes that child-led research is feasible with young children, but the research process can include or exclude certain forms of children’s communication, making some children more ‘competent’ to undertake research than others.
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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 | 0.533 | 0.380 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.025 | 0.147 |
| Scholarly communication | 0.053 | 0.056 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".