Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for their ongoing support in reviewing manuscripts for Human Development:Jedediah W.P. Allen, Ankara, TurkeyRamesh Balasubramaniam, Merced, CA, USANancy Budwig, Worecester, MA, USAJeremy Ian Maxwell Carpendale, Burnaby, BC, CanadaAnthony Chemero, Cincinnati, OH, USAHanne Collins, Los Angeles, CA, USAKarin Frey, Seattle, WA, USAJohn Geldhof, Corvallis, OR, USACarolina Goncalves, Medford, OR, USAGary Greenberg, Wichita, KS, USAMarie Hennecke, Bochum, GermanyYeh Hsueh, Memphis, TN, USAJana Iverson, Boston, MA, USAOlga Kornienko, Fairfax, CA, USADaniel Lapsley, Notre Dame, IN, USARichard M. Lerner, Medford, OR, USACharlie Lewis, Lancaster, UKRobert Lickliter, Miami, FL, USAZihan Liu, Springfield, IL, USAJeffrey J. Lockman, Austin, TX, USALukas Lopez, Salt Lake City, UT, USALucía Magis-Weinberg, Seattle, WA, USAMichael F. Mascolo, North Andover, MA, USAAnn Masten, Minneapolis, MN, USAGeorge Michel, Greensboro, NC, USARobert Mirski, Torun, PolandDavid S. Moore, Claremont, CA, USAUlrich Mueller, Victoria, BC, CanadaChris Napolitano, Champaign, IL, USADarcia Narvaez, Notre Dame, IN, USAJessica Navarro, Elon, NC, USAAmy Needham, Nashville, TN, USAWillis Overton, Philadelphia, PA, USAAlik Palatnik, Jerusalem, IsraelJoanna Peplak, Burnaby, BC, CanadaNorma Perez-Brena, Tucson, AZ, USACatherine Raeff, Philadelphia, PA, USAMarc J. Ratcliff, Geneva, SwitzerlandChristina Röcke, Zurich, SwitzerlandJudith Smetana, Rochester, MN, USATeresa Tavassoli, Reading, UKJonathan M. Tirrell, Medford, OR, USAElliot Turiel, Berkeley, MI, USAMaarten Vansteenkiste, Ghent, BelgiumKristia Wantchekon, Washington, DC, USAZheng Yan, Albany, NY, 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 | 0.255 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.276 | 0.203 |
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".