Characterizing implicit information during peer review meetings
Bibliographic record
Abstract
Article Free Access Share on Characterizing implicit information during peer review meetings Authors: Patrick d'Astous École Polytechnique de Montréal C.P. 6079, Suc. Centre-ville, Montréal, Canada H3C 3A7 École Polytechnique de Montréal C.P. 6079, Suc. Centre-ville, Montréal, Canada H3C 3A7View Profile , Pierre N. Robillard École Polytechnique de Montréal C.P. 6079, Suc. Centre-ville, Montréal, Canada H3C 3A7 École Polytechnique de Montréal C.P. 6079, Suc. Centre-ville, Montréal, Canada H3C 3A7View Profile Authors Info & Claims ICSE '00: Proceedings of the 22nd international conference on Software engineeringJune 2000 Pages 460–466https://doi.org/10.1145/337180.343189Published:01 June 2000Publication History 4citation339DownloadsMetricsTotal Citations4Total Downloads339Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".