Bibliographic record
Abstract
My knowledge of the inner workings of CSSH began when the journal had completed its sixth year and was already well known and widely circulated. A few years before, Sylvia L. Thrupp had brought it with her when she moved to Michigan from the University of Chicago. When I joined the Michigan faculty, its presence was one of the attractions. I had been become aware of CSSH in its first year, pointed to it by Robert R. Palmer, who was working on the second volume of his Age of the Democratic Revolution. As we talked about his project, I expressed surprise that more was not being done with historical comparison, and he mentioned, rather skeptically I thought, that there was a new journal I might be interested in. I found the first issues in the library and mentioned them to another Princeton colleague who commented, “When historians don't know enough to write about one place, they write on two.” So I learned from the first that the venture was controversial. In contrast, Joseph R. Strayer told me he thought the new journal very promising, largely because of the woman who had founded it. (Much later, I learned that he had persuaded Princeton to make a small contribution to the journal's founding, and some twenty-five years after that conversation, when both were in their eighties, Joseph Strayer married Sylvia Thrupp).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".