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
Despite the drastically different settings, cultures, incentive systems, time pressures, etc., we find that the parameters of peer review converge in contemporary software projects. We examine two Google lead projects, Android and Chrome, three Microsoft projects, Bing, Office, and MS SQL, and one project internal to AMD. We contrast our findings with data taken from traditional software inspection conducted on a Lucent project, a compiler, and from open source software peer review on six projects, including Apache, Linux, and KDE. Our measures include the review interval, the number of developers involved in review, and proxy measures for the number of defects found during review. We also introduce a measure of the degree to which knowledge is shared during review, an aspect of review practice that has traditionally only had experiential support. Our knowledge sharing measure shows that conducting peer review increases the number of distinct files a developer knows about by 66% to 150% depending on the project. This paper represents one of the first studies of contemporary review in software firms and the most diverse study of peer review to date. We discuss the practices that converge among projects as well as any divergent and anomalous practices.
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 | 0.001 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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