Correction: Social epidemiology of early adolescent problematic screen use in the United States
Bibliographic record
Abstract
Authors and Affiliations Division of Adolescent and Young Adult Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA Jason M. Nagata & Gurbinder Singh Geisel School of Medicine, Dartmouth College, Hanover, NH, USA Omar M. Sajjad Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada Kyle T. Ganson Department of Management, Policy and Community Health, University of Texas Health Science Center at Houston, Houston, TX, USA Alexander Testa Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA Dylan B. Jackson Department of Family Medicine, College of Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA Shervin Assari Department of Urban Public Health, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA Shervin Assari Marginalization-related Diminished Returns (MDRs) Research Center, Charles R. Drew University of Medicine and Science, Los Angeles, CA, USA Shervin Assari Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, USA Stuart B. Murray Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA Kirsten Bibbins-Domingo Center for Health Sciences, SRI International, Menlo Park, CA, USA Fiona C. Baker School of Physiology, University of the Witwatersrand, Johannesburg, South Africa Fiona C. Baker Authors Jason M. Nagata View author publications You can also search for this author in PubMed Google Scholar Gurbinder Singh View author publications You can also search for this author in PubMed Google Scholar Omar M. Sajjad View author publications You can also search for this author in PubMed Google Scholar Kyle T. Ganson View author publications You can also search for this author in PubMed Google Scholar Alexander Testa View author publications You can also search for this author in PubMed Google Scholar Dylan B. Jackson View author publications You can also search for this author in PubMed Google Scholar Shervin Assari View author publications You can also search for this author in PubMed Google Scholar Stuart B. Murray View author publications You can also search for this author in PubMed Google Scholar Kirsten Bibbins-Domingo View author publications You can also search for this author in PubMed Google Scholar Fiona C. Baker View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Jason M. Nagata .
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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 | 0.006 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.093 | 0.032 |
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".