Tech-social synergy: nurturing community well-being
Bibliographic record
Abstract
Journal Article Tech-social synergy: nurturing community well-being Get access Lucky Ihaura, Lucky Ihaura Departement of Early Childhood Education Teacher Education, Faculty of Education and Vocational, Universitas Lancang Kuning, Riau 28266, Indonesia Address correspondence to Lucky Ihaura, E-mail: lucky@unilak.ac.id. Search for other works by this author on: Oxford Academic PubMed Google Scholar Dwi Sri Rahayu, Dwi Sri Rahayu Department of Guidance and Counseling, Faculty of Training and Education, Universitas Katolik Widya Mandala Surabaya-Kampus Kota Madiun, 63131, Indonesia https://orcid.org/0000-0002-4078-6533 Search for other works by this author on: Oxford Academic PubMed Google Scholar Sean Marta Efastri, Sean Marta Efastri Departement of Early Childhood Education Teacher Education, Faculty of Education and Vocational, Universitas Lancang Kuning, Riau 28266, Indonesia Search for other works by this author on: Oxford Academic PubMed Google Scholar Felix Trisuko Nugroho Felix Trisuko Nugroho Department of Guidance and Counseling, Faculty of Training and Education, Universitas Katolik Widya Mandala Surabaya-Kampus Kota Madiun, 63131, Indonesia Search for other works by this author on: Oxford Academic PubMed Google Scholar Journal of Public Health, fdae045, https://doi.org/10.1093/pubmed/fdae045 Published: 31 March 2024 Article history Received: 24 January 2024 Accepted: 18 March 2024 Published: 31 March 2024
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.016 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".