RefSponse: A Literature Evaluation System for the Professional Astrophysics Community
Bibliographic record
Abstract
We describe an implementation of a semi-automated review system for the astrophysics literature. Registered users identify names under which they publish, and provide scores for individual papers of their choosing. Scores are held confidentially, and combined in a weighted average grade for each paper. The grade is divided among the co-authors as assigned credit. The credit accumulated by each user (their ``mass'') provides the weight by which their score is averaged into papers' grades. Thus, papers' grades and users' masses are mutually dependent and evolve in time as scores are added. Likewise, a user's influence on the grade of a paper is determined from the perceived original scientific contribution of all the user's previous papers. The implementation, called RefSponse -- currently hosted at http://bororo.physics.mcgill.ca -- includes papers in astro-ph, the ApJ, AJ, A&A, MNRAS, PASP, PASJ, New Astronomy, Nature, ARA&A, Phys. Rev. Letters, Phys. Rev. D. and Acta Astronomica from 1965 to the present, making extensive use of the NASA/ADS abstract server. We describe some of the possible utilities of this system in enabling progress in the field.
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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 | 0.048 | 0.149 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.043 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.028 |
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".