Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for the ongoing support in reviewing manuscripts for Human Development:Dor Abrahamson, Berkeley, CA, USAAileen Aldalur, Rochester, NY, USAJedediah W.P. Allen, Ankara, TurkeyWilliam Arsenio, New York, NY, USAParissa Ballard, Winston-Salem, NC, USAHanna Bertilsdotter Rosqvist, Huddinge, SwedenJanet Boseovski, Greensboro, NC, USANancy Budwig, Worcester, MA, USAJeremy Trevelyan Burman, Groningen, The NetherlandsChristy Byrd, Raleigh, NC, USALinda Camras, Chicago, IL, USALiz Chesworth, Sheffield, UKAnna Ciaunica, Lisbon, PortugalBrad Cokelet, Lawrence, KS, USABrian D. Cox, Hempstead, NY, USAColette Daiute, New York, NY, USASusan Engel, Williamstown, MA, USAKim Theresa Ferguson, Yonkers, NY, USAElizabeth Finnegan, Sparkill, NY, USARobyn Fivush, Atlanta, GA, USADaniel Fobi, Leeds, UKBlaine Fowers, Miami, FL, USAKarin Frey, Seattle, WA, USASimona Ghetti, Davis, CA, USAAmanda Guyer, Davis, CA, USASinead Harmey, London, UKDaniel Hart, Camden, NJ, USAYeh Hsueh, Memphis, TN, USASara Incao, Genoa, ItalyJamie Jirout, Charlottesville, VA, USAJuliana Karras, San Francisco, CA, USADaniel Kelly, West Lafayette, IN, USAOlga Kornienko, Fairfax, VA, USAZihan Liu, Springfield, IL, USACaitlin Mahy, St. Catharines, ON, CanadaMichael F. Mascolo, North Andover, MA, USAGeorge Michel, Greensboro, NC, USAUlrich Mueller, Victoria, BC, CanadaUtsa Mukherjee, London, UKSusan Murphy, Edinburgh, UKJessica Navarro, Elon, NC, USAMarkus Paulus, Munich, GermanyJoanna Peplak, Burnaby, BC, CanadaPrithvi Perepa, Birmingham, UKLawrence Pick, Washington, DC, USAMarc J. Ratcliff, Geneva, SwitzerlandSusan Rivera, College Park, MD, USAJason Robert, Tempe, AZ, USAChristina Röcke, Zurich, SwitzerlandHoward Steele, New York, NY, USAKristia Wantchekon, Washington, DC, USA
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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 | 0.026 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.256 | 0.192 |
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".