Bibliographic record
Abstract
I find myself in an ambiguous pose that I need to explain before I continue. I did more than count Betty Jo Dobbs as my friend. Rightly or wrongly, I regarded myself as her earliest admirer in the discipline – after her graduate professors, to be sure. We got into correspondence, first about Newton's handwriting, and then about his alchemical papers, while she was still a graduate student, and as a consequence I read her dissertation chapter by chapter as she completed each one. I wrote enthusiastically to Northwestern in support of her first appointment, and I continued to write whenever she needed a reference. I feel her loss both personally and professionally. For all that, I am going to devote this chapter, written in her memory, to taking issue with the last piece she published, her History of Science Society Lecture, “Newton as Final Cause and First Mover.” Anyone who knew Professor Dobbs understood that you did not take a high tone with her. Neither do I here, even when there is no possibility of a retort. You also understood that she took the intellectual life seriously and thought that important issues required thorough discussion. I can do no better service to her memory than to take the issues she raised seriously and, in a low voice, to contribute what I can to their elucidation. The issues concern the Scientific Revolution, and I intend to make that topic, rather than details of Dobbs's essay, my focus.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".