The Kids Do-It-Yourself Media Partnership 2013-2018 Summary Report of Key Findings
Bibliographic record
Abstract
Where children’s creations used to be relegated to refrigerator doors and classroom bulletin boards, they can now be shared with an audience of millions thanks to connected digital technologies. Between 2013 and 2018, the Kids DIY Media Partnership looked at how and where children create and share media online, and at the designs, regulations, infrastructures, and technologies that underpin the platforms kids use. Our focus was on exploring the opportunities and challenges associated with kids’ DIY media, and with finding ways to best foster a rights based, inclusive, child-centric approach to children’s online media-making and sharing. Working with Canadian and American academics, designers, media producers, child advocates, educators, and NGOs, we identified many strengths in the kids’ DIY media landscape. We also spotted some areas for improvement. Our project began with a content analysis of 140 websites where children can share everything from fanfiction to computer programs, physical media to digital videos. We also looked at the laws and regulations that govern these websites. Subsequently, we conducted seven case studies of exceptional, productive models of children’s digitally connected DIY media production and participation: Algodoo, DIY.org, Gamestar Mechanic, Roblox, Scratch, Storybird, and Tate Kids. We conducted focus groups with children who use DIY platforms and held workshops with adults who design them. Together, all of this information has resulted in the recommendations provided here: research-based, user-supported best practices for designing DIY media platforms aimed at (or inclusive of) children. Our research has shown that there is no single best way to support children’s media making. As a result, we propose a range of principles to consider when designing for and with children. We begin with ways to improve creation, sharing, collaboration, civic engagement, selfrepresentation, and education; next, we turn to legal concerns such as child-friendly privacy policies and copyright regulations; finally, we consider how platforms can be child-friendly and age-appropriate.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".