Children describing the world: Mixed-method research by child practitioners developing an intergenerational dialogue
Bibliographic record
Abstract
Children are becoming increasingly engaged in the practice of research, either as active collaborators with adults or as independent researchers in their own right. This paper explores aspects of training and mentoring of children engaged in research practice as independent researchers, and highlights the use of creative methodologies in mixed-method research undertaken by children. Three primary aspects of participation and training are considered in relation to the space children inhabit in the research community: the ways in which children acquire research skills and the ways in which children are mentored in their research practice; the use of creative methods as conceptual and interpretive tools in interdisciplinary mixed-method research and how creative methodologies may benefit and empower child practitioners; and thirdly the importance of dissemination of research undertaken by children, and the quality of the intergenerational dialogue emerging from it. The paper begins with a story. The story is a personal observation translated into narrative and placed here to contextualise (rather than analyse) the research work undertaken by a group of Australian children, concurrent with their counterparts in the UK and Canada, over an 18-month period between April 2011 and September 2012. It introduces a methodological framework that was the underpinning of the project designed by the children and mentored by the author.
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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 | 0.082 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".