Bibliographic record
Abstract
Introduction Joseph M. Hawes (University of Memphis, USA) and N. Ray Hiner (University of Kansas, USA) 1 Family Relationships David Barrett and Maria Kukhareva (both University of Bedfordshire, UK) 2 Community Mona Gleason and Veronica Strong-Boag (both University of British Columbia, CANADA) 3 Economy Lisa Jacobson and Erika Rappaport (both University of California-Santa Barbara, USA) 4 Environment Pamela Riney-Kehrberg (Iowa State University, USA) 5 Education William J. Reese (University of Wisconsin-Madison, USA) 6 Life Cycle Katherine Jellison (Ohio University, USA) 7 The State Kriste Lindenmeyer (University of Maryland, USA) and Jeanine Graham (University of Waikato, NEW ZEALAND) 8 Faith and Religion Jon Pahl (Lutheran Theological Seminar at Philadelphia, USA) 9 Health and Science Doug Imig and Frances Wright (both University of Memphis, USA) 10 World Contexts Jeanine Graham (University of Waikato, NEW ZEALAND) Notes Bibliography Contributors Index
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".