Bibliographic record
Abstract
EDITOR'S NOTE We acknowledge with great appreciation and gratitude the contributions of our long-term bibliographer Szilvia Szmuk, who, with an impressive group of international collaborators, performed an invaluable service to the Bulletin of the Comediantes. We thank her for her devotion to the journal and regret that she will be leaving this position. We wish her the very best always. Our sincere thanks also to Margaret R. Greer, who served a three-year term as associate editor. Meg will continue as a member of the advisory board. We are happy to announce the appointment of Bradley Nelson as assistant editor of BCom. Brad Nelson, who received his PhD from the University of Minnesota, currently is assistant professor of Spanish at Concordia University in Montreal. Starting with this volume of the journal, Robert Lauer will report on the annual Chamizal theater festival. We are most grateful for his insights, and, of course, we will remember with fondness the work of his predecessor, the late Kenneth Stackhouse. We thank those who have contributed to this number of the journal, and, as always, we encourage Comedia scholars to submit the fruits of their research to the Bulletin of the Comediantes. E.H.E ...
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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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.041 | 0.029 |
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".