Inclusive Child Engagement in HCI: Exploring Ocean Health with Schoolchildren
Bibliographic record
Abstract
In a ten-week project with nine school classes across the North West of England we explored ocean health with IT-enabled solutions. We describe the activities carried out under headings of participation, learning, and design. Participation activities, which included recruitment, focused on setting the parameters for children’s inclusion and ensuring they understood how data might be used, and that handing in artefacts to the research team was their choice. Learning happened in an environment of contextual relevance that enabled children to develop data literacy whilst we could explore relevant research questions. Design was a journey from individual to whole-class design, while developing engineering thinking and social cohesion. We reflect on the journey showing that children learned from the activities and acquired a new enthusiasm for their local coastline. We reflect on how our inclusive approach can broaden HCI research to wider communities of children and encourage others to apply our model.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".