Herbert west: deanonymizer
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.
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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.163 | 0.149 |
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".