Bibliographic record
Abstract
For almost 40 years, the British jurist and Fellow of the Royal Society Taylor White (1701–1772) actively engaged in commissioning artists to paint plants and animals for his ‘paper museum’. White amassed a collection of almost 1000 drawings of birds, mammals, fish, amphibians and reptiles, acquired by McGill University in 1927. His first recorded purchase was a watercolour by George Edwards (1694–1773). He also acquired works from Eleazar Albin ( fl. 1690– ca 1742) and Jacob van Huysum ( ca 1687–1740), but the majority of the watercolours were painted by two artists, Charles Collins ( ca 1680–1744) and Peter Paillou ( ca 1712–1782). In 2018 a research group at McGill University Library received funding from the Social Sciences and Humanities Research Council of Canada for the project ‘Undescrib'd: Taylor White's paper museum’. The project produced a complete catalogue of the White collection, including attribution of all unsigned works, and digitized all paintings and notes. This paper documents the process surrounding the original creation of the collection, reviewing the careers of the artists and White's relationship with them, the value of the commissions and the challenges of painting natural history subjects. It also describes the mechanics of painting, including pigments, papers used and artists' techniques.
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 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 | 0.001 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".