Wilbur R. Knorr on Thābit ibn Qurra: A Case-Study in the Historiography of Premodern Science
Bibliographic record
Abstract
There was a widespread belief among historians of science of my generation that high competence with regard to content and languages alone can guarantee better, more reliable results than can good philology combined with high competence in history or the other human sciences. In my casestudy of Wilbur R. Knorr’s analysis of several medieval Arabic and Latin texts on the balance, or steelyard, I highlight a variety of factors that compromised time and again his understanding and interpretation of his chosen texts. I conclude that a greater openness to more complex historiographical assumptions and more sophisticated methodological approaches as well as a greater willingness to contextualize documents in numerous dimensions before coming to conclusions about their specific meaning is crucial if we are to correct and improve upon work such as Knorr’s analysis of the Kitab al-qarastun, ascribed to Thābit ibn Qurra, and the Liber de canonio. The way forward is to enhance and temper philological analysis with solid analysis of scientific content within its relevant contexts. Published Online (2021-04-30)Copyright © 2021 by Sonja Brentjes Article PDF Link: https://jps.library.utoronto.ca/index.php/aestimatio/article/view/37604/28609 Corresponding Author: Sonja BrentjesMPIWG, BerlinE-Mail: brentjes@mpiwg-berlin.mpg.de
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".