Bibliographic record
Abstract
Eastern Studies Department, and particularly to the Hebrew Studies faculty for their abundant support over my years of study in graduate school. I am especially grateful to the readers of my dissertation for many invaluable suggestions and many helpful critiques. My advisor, Professor Harold Liebowitz, has been my guide, my mentor, and my academic role model throughout the graduate school journey. He exemplifies the spirit of patience, thoughtful listening, and a true love of learning. Many thanks go to my readers, professors Esther Raizen, Avraham Zilkha, Aaron Bar-Adon, and Kristen Lindbeck (of Florida Atlantic University), each of whom has been my esteemed teacher and shared his or her special area of expertise with me. Thank you to Graduate Advisor Samer Ali and the staff of Middle Eastern Studies, especially Kimberly Dahl and Beverly Benham, for their encouragement and assistance. Thank you to my colleagues at California State University, Chico, particularly to Andrea Lerner and Sam Edelman for leading me in independent studies. Jed Wyrick, chairperson of Religious Studies was especially generous with his time, providing guidance in both research and organization. Thanks also go to the Graduate Theological
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.248 | 0.138 |
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".