2022–2023 Medical Education Planning Committee
Bibliographic record
Abstract
Chair Lara Varpio, PhD Codirector of Research in Medical Education Children’s Hospital of Philadelphia Members Irene Alexandraki, MD, MPH Senior Associate Dean of Academic Affairs University of Arizona College of Medicine–Phoenix Jonathan M. Amiel, MD Associate Professor of Psychiatry Columbia University Medical Center Justin L. Bullock, MD, MPH Nephrology Research Fellow University of Washington Jefferey J.H. Cheung, PhD Assistant Professor, Medical Education University of Washington Jorie Colbert-Getz, PhD, MS Assistant Dean of Education Quality Improvement University of Utah School of Medicine Gary Beck Dallaghan, PhD Assistant Dean, Evaluation & Assessment University of Texas Tyler School of Medicine Deborah Engle, EdD, MS Assistant Dean for Assessment and Evaluation Duke University Reena Karani, MD, MHPE Professor Icahn School of Medicine at Mount Sinai Katie Schultz, MD, MEd, FAAP PhD student, Institute of Health Sciences Education Research Associate, Department of Emergency Pediatric Medicine McGill University Jonathon Sherbino, FAcadMED, MD, MEd Professor of Medicine McMaster University Cayla R. Teal, PhD, MA Education Associate Professor, Population Health University of Kansas Medical Center AAMC Staff Ana Henriquez Constituent Engagement Specialist Academic Affairs Association of American Medical Colleges Katherine McOwen Senior Director, Educational and Student Affairs Academic Affairs Association of American Medical Colleges Lynn Shaull Senior Research Analyst Academic Affairs Association of American Medical Colleges
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.377 | 0.230 |
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".