Bibliographic record
Abstract
In addition to the members of the Editorial Advisory Board the following reviewers collaborated in 2017: Juan Ignacio Asensio Pérez University of Valladolid, Spain Rita Bencivenga UMR-LEGS - CNRS, France Giovanni Bonaiuti University of Cagliari, Italy Isabella Bruni University of Florence, Italy Barbara Caci University of Palermo, Italy Maria Concetta Carruba Catholic University of Milan, Italy Graziano Cecchinato University of Padua, Italy Manuela Delfino Secondary School “Don Milani”, Genoa, Italy Ottavia Epifania University of Padua, Italy Floriana Falcinelli University of Perugia, Italy Alison Fox Open University, United Kingdom Maria Antonietta Impedovo University of Aix-Marseille, France Kamini Jaipal Jamani Brock University, Canada Pierpaolo Limone University of Foggia, Italy Giorgio Olimpo Institute for Educational Technology - CNR, Italy Corrado Petrucco University of Padua, Italy Fabrizio Ravicchio Institute for Educational Technology - CNR, Italy Anna Serbati University of Padua, Italy Manuela Simeon Ca’ Foscari University of Venice, Italy Aggeliki Tzavara University of Patras, Greece
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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 | 0.016 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.386 | 0.319 |
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".