Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The vast majority of scientific journal, conference, and grant selection processes withhold the names of the reviewers from the original submitters, taking a bettersafe-than-sorry approach for maintaining collegiality within the small-world communities of academia. While the contents of a review may not color the long-term relationship between the submitter and the reviewer, it is best to not require us all to be saints. This paper raises the question of whether the assumption of reviewer anonymity still holds in the face of readily-available, high-quality machine learning toolkits. Our threat model focuses on how a member of a community might, over time, amass a large number of unblinded reviews by serving on a number of conference and grant selection committees. We show that with access to even a relatively small corpus of such reviews, simple classification techniques from existing toolkits successfully identify reviewers with reasonably high accuracy. We discuss the implications of the findings and describe some potential technical and policy-based countermeasures. 1
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it