Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Acknowledgements I wish to thank all the authors for contributing their work to this project. Thanks are also due to the reviewers for their critical remarks and constructive comments, which certainly helped to improve quality of the presented papers. So, special thanks to the reviewers of the papers for this special issue: Professor Dr. Kai Hauser (Technical University of Berlin, Germany) Professor Dr. Sajahan Miah (Dhaka University, Bangladesh) Dr. Christopher Pincock (Purdue University & University of Pittsburgh, United States) Professor Dr. Erhard Scholz (University of Wuppertal, Germany) Dr. Michael Silberstein (Elisabethtown College, United States) Professor Dr. Lawrence M. Ward (Univeristy of British Columbia, Vancouver, Canada) Thanks are also due to the editors of Philosophical Psychology, Cees van Leeuwen and William Bechtel, for creating the possibility to publish this collection of papers in their journal; and last but not the least, to the editorial office of the journal for their helpful assistance and excellent support during the final stages of the publication process. Notes [1] The symposium was embedded as a theme session in the Fechner Day conference, held in Tokyo, October 2007. For external reasons, only four papers could be really presented at the symposium, but all five papers were printed in the proceedings book: Mori, S., Miyaoka, T., & Wong, W. (Eds.), Fechner Day 2007, Tokyo: International Society for Psychophysics.
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 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; both teacher heads 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".