Children's Media Making, but Not Sharing: The Potential and Limitations of Child-Specific Diy Media Websites
Bibliographic record
Abstract
From drawing pictures to making home movies, children have long produced their own, do-it-yourself (DIY) media at the individual and local scales. Today, children's DIY media creation increasingly takes place online, using digital technologies and tools that allow them to not only produce but also share their ideas with the world. This article relays findings from the first stages of a three-year inquiry project into the opportunities and challenges associated with the rise of children's online DIY media: an extensive media scan to identify websites and an in-depth content analysis of the terms and conditions, privacy policies and overall site designs. Among our key findings is the discovery that a narrow emphasis on making and a systematic disregard for the crucial role of sharing predominate the current children's online DIY media environment. Furthermore, corporate ownership claims and a lack of features aimed at enabling user interaction often diminish the sites' potential to advance children's cultural rights and educational opportunities. We conclude that a disproportionate emphasis on making as a form of individualised learning has led to an undermining of crucial dimensions of children's DIY media.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".