Scaling internet research publication processes to internet scale
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 reviewing process used by most computer systems conferences originated in pre-Internet days. In this process, authors submit papers that are anonymously reviewed by program committee (PC) members and their delegates. Reviews are single-blind: reviewers know the identity of the authors of a paper, but not vice versa. At the end of the review process, authors are informed of paper acceptance or rejection and are also given reviewer feedback and scores. Authors of accepted papers use the reviews to improve the paper for the final copy, and the authors of rejected papers use them to revise and resubmit them elsewhere, or withdraw them altogether. Recently, some conferences have tried two minor innovations. With double-blind reviewing, reviewers do not know (or, at least, pretend not to know) the authors. And, with “shepherding”, a PC member ensures that authors of accepted papers with minor flaws make the revisions required by the PC. Surprisingly, the advent of the Internet has scarcely changed the reviewing process. Everything proceeds as before, except that papers and reviews are submitted online or by email, and the paper discussion process, at least for secondtier conferences and workshops, does not require the physical presence of the PC. A naive observer, seeing the essential structure of the reviewing process preserved with such verisimilitude, may come to the conclusion that the process has achieved perfection, and that is why the Internet has had so little impact. Such an observer would be, sadly, rather mistaken. We argue that the current reviewing process is fundamentally flawed, with at least five systemic problems that undermine the integrity of the process (Section 2). A gametheoretic modeling, presented in Section 3, demonstrates that these visible symptoms are due to inherent conflicts in the underlying incentive structure. Understanding the incentive structure allows us to design several incentivecompatible mechanisms, presented in Section 4, that remedy nearly all the problems we identified.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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