THE DILEMMAS OF DIGITAL METHODOLOGIES: LEARNING FROM WORK ON "YOUNG DIGITAL"
Bibliographic record
Abstract
This article explores common dilemmas facing researchers and practitioners who wish to use digital media in research with children and young people. The article explores both cultural-social-economic and material approaches to digital media. These draw attention to five areas, explored in the article, which raise particular dilemmas and opportunities: networked mobility; interoperability and convergence; corporate involvement; confidentiality, anonymity and privacy; and intellectual property and moral rights. When involving children and young people through digital media, the boundaries between online and offline worlds are increasingly blurred, raising practical and ethical dilemmas. The article concludes that research with children and young people needs to take account of the socio-cultural norms in using digital media and that the tenets of ethical research still apply.
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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 | 0.082 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.025 | 0.093 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".