Rationale, Design and Methods Protocol for Participatory Design of an Online Tool to Support Industry Service Provision Regarding Digital Technology Use ‘with, by and for’ Young Children
Bibliographic record
Abstract
Adults who educate and care for young children are exposed to mixed-messages about what is in the best interests of young children in digital society. Such mixed-messaging makes adult decision-making about technology use in the best interests of young children hard to achieve. This project addresses this problem by working with leading organisations providing services related to quality digital media production, online-safety education, digital play and digital parenting. Using a Participatory Design approach, families, educators, industry partners and researchers will conduct mixed-methods investigations concerning: Relationships; Health and Well-being; Citizenship; and Play and Pedagogy to identify practices concerning technology use 'with, by and for' young children. Iterative design cycles will develop an Online Tool to support organisations providing services to young children and the adults responsible for their education and care. As society becomes more digital families and educators need new knowledge about what people do in digital society to inform their decision-making. This project will support organisations to use an empirically informed approach to service provision regarding using technologies in the best interests of young children.
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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 | 0.224 | 0.139 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.070 | 0.014 |
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".