Taylor White's ‘paper museum’ (1725–1772): understanding the scientific work of an unpublished naturalist
Bibliographic record
Abstract
From approximately his election as a Fellow of the Royal Society in 1725 to his death in 1772, as the London barrister Taylor White (1701–1772) moved up the legal ladder, he commissioned, gathered, and organized a tremendous collection of zoological paintings now held in the Blacker Wood Collection of McGill University Rare Books and Archives. As White did not publish any major work during his lifetime, he has been substantially ignored in the historiography of science. By investigating the considerable painting compilation available in the collection, this article aims to understand White's scientific practice as a naturalist, working primarily from non-textual primary sources. The taxonomical work comprises the global arrangement of the plates, and the referencing practice, as well as the limited correspondence available on the English barrister, and these help to position the anonymous Taylor White within the world of naturalists at that time.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".