Bibliographic record
Abstract
There is a prevalent myth, even in scholarly literature, that peer review was born, fully formed, with the advent of the first scientific journals in the seventeenth century. Recent work has shown this to be false. Many of the practices we call peer review are much newer—as new as the second half of the twentieth century. Some essential elements of peer review, however, are much older than the seventeenth century—a fact that has been neglected, both by those who have propagated the myth and also by those who have more recently sought to dispel it. This paper provides three examples of scholarly review from history. The first is an example of editorial review in ancient Rome. The second is an example of post-publication peer review involving scholia, beginning in the fourth century. The third is an example of pre-publication review by censors in the sixteenth to eighteenth centuries. I join with those authors who seek to bust the myth about the origins of scholarly review but do so by extending their work in the opposite direction chronologically. What we now give the name peer review is really a group of things that has evolved over time. If we want to learn from the history of scholarly review, then we should take a broader and longer view.
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 | 0.105 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.047 | 0.033 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".