Fruitful collaborations: the Taylor White project in the Blacker Wood Natural History Collection
Bibliographic record
Abstract
As part of a themed print issue of Notes and Records dedicated to a research project surrounding the eighteenth-century Taylor White collection of animal paintings, this article provides context by describing the initial acquisition of the collection, and by situating it within the larger Blacker Wood Natural History Collection held at McGill University Library. Highlights of the Blacker Wood Collection are discussed, along with the collection's founder, Dr Casey Wood. The second part of the article provides a brief examination of the movement, in some academic administrative circles, towards the ‘de-professionalization’ of librarian work within academic libraries, and offers an outline of the specialized skills that librarians bring to the description, analysis and preservation of special collections. The Taylor White Project is then offered as an example of research collaborations between scholars and librarians; a description of the advantages of embedding a scholar within specific library collections to work with, rather than replace, a librarian is provided. The author suggests this strategy as one potential answer to the question of ‘de-professionalization’, to move away from divisive discussions towards a more symbiotic relationship between scholars and librarians.
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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 | 0.038 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.049 | 0.028 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".