Children as researchers: Learning from children's art on inclusion
Bibliographic record
Abstract
Allowing children to express their opinions and ideas through drawing can be a useful way to engage children as coresearchers. This study explored children’s understanding of inclusion and what it means to be socially excluded and used multiple ways to explore children’s understanding including interviews, focus groups (using semi-structured interviews), and children’s drawings. Data was collected from children in Grade 2 (two groups), and children in Grade 4 (one group); and was analyzed using concept mapping, where the children as coresearchers were involved in analyzing their own data into themes. Themes that emerged addressed using play as a means of including children with exceptionalities, normalizing exceptionalities, celebrating individual differences, and teaching acceptance and inclusion in the school setting. This study highlighted the important of using alternative ways of gathering data (such as drawings) as children do not always verbalize, or know how to verbalize, their thoughts and ideas.
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".