Bibliographic record
Abstract
Financial support for this research was provided by a generous grant from the Wallace Foundation. The findings, conclusions and recommendations herein are those of the authors and do not represent the official positions or policies of the funder or of the educational institutions of the researchers. Copyright ©2010 by the University of Minnesota. ® All rights reserved. About the Organizations The Center for Applied Research and Educational Improvement (CAREI) at the University of Minnesota links empirical research to real-world applications for educational leaders in Minnesota and across the United States. To do so, CAREI conducts comprehensive studies that provide information about challenges confronting schools and practices leading to educational improvement. For information on our technical reports and resources, please visit our Web site: www.cehd.umn.edu/CAREI/ The Ontario Institute for Studies in Education at the University of Toronto (OISE/UT) is the largest professional school of education in Canada and among the largest in the world. It offers initial teacher education, continuing education, and graduate programs, all sustained by faculty who are involved in research across the spectrum of issues connected with learning. Please visit our Web site for more information: www.oise.utoronto.ca
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.395 | 0.164 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".