Editorial: Control alt delete – technology and children's mental health
Bibliographic record
Abstract
With international contributions from Denmark, Peru, Italy, Turkey, Estonia, Russia, Canada, the USA, Australia and the UK, this special issue offers insights and evidence about the technology's ability to act as a force of good and a source of harm for young people's mental health. As we better understand the complex and bidirectional relationship between technology and mental health, we need to move beyond dichotomous narratives about it being good or bad; it is both, depending on how it is used. Collective responsibility across technology companies, researchers, public services and community organisations, parents and the young people themselves can make a difference in the way technology is used to protect and improve mental health.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.019 | 0.013 |
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".