Bibliographic record
Abstract
We also want to thank our dedicated section editors for their commitment to this Handbook.We thank you for your ongoing support, writing and curating the sections of this book.You not only helped find the numerous contributors, but your perceptions and editing skills made each chapter and section better.Furthermore, you were indomitable in collecting manuscripts and helping us focus on the central issues.This Handbook is the result of the brilliant and diverse contributions of all the authors of the 92 chapters included herein.We thank you for sharing your critical thoughts, insights, and experiences with us and our readers.We deeply appreciate your openness and vulnerability in participating in this project, as well as your creative expressions.Many of you come from diverse cultural and national backgrounds.We thank you for your willingness to adapt to a single format and style.The Handbook would not have been possible without the tremendous support of our publisher -Springer, in particular the efforts and responsiveness of Shobana Lenin and Juby George.We particularly wish to thank you for picking up the threads and reviving the project after a 2-year delay because of Marcia's death.We thank you for making the Meteor system accessible to our authors (even though the co-editors in chief were initially doubtful of the necessity for the Meteor
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".