Re-Imagining Research Partnerships: Thinking through "Co-research" and Ethical Practice with Children and Youth
Bibliographic record
Abstract
Intentions to co-research and engage in participatory research pervade education and social science research with children and particularly research on engagement in digital spaces, with digital tools. Starting in the 1900s, there were many attempts to explicitly describe co-research methods and intentions in education but recently co-research has been used in a more taken-for granted way. Using snapshots from three research projects, I trouble my own attempts at co-research. Firstly, in a two-year ethnographic study, research positions were shifted by following the children’s lead and multimodal textmaking interests. Secondly, in an arts-informed classroom study of family photography and family stories, the ways in which the children understood the research process, and gave or withheld assent, influenced how they engaged as co-researchers. Finally, a larger comparative arts-informed study of youths’ digital practices in Hamilton is explored with an eye to how co-research evolved for the youth throughout the project. None of these projects were designed to engage with co-research in a comprehensive way. Yet, across these snapshots, a more nuanced understanding of co-research is envisioned; one that involves reflexive ethical practice and an emergent and attentive focus on consent.
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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 | 0.137 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.183 |
| Scholarly communication | 0.035 | 0.044 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".