Cataloguing power: delineating ‘competent naturalists’ and the meaning of species in the British Museum
Bibliographic record
Abstract
At the centre of nineteenth-century imperial authority sat the British Museum, which set the standard for discourse about natural history. This paper examines the meaning of those standards, exploring their important but little-understood role in ending the nineteenth-century species debate. The post-Reform Bill political assault on the authority of the British Museum is examined in the light of the ‘species problem’, and a surprising solution by John Edward Gray, keeper of the natural history collection, is seen as both a mediation and a closure of disputes over the meaning of species and the competence of naturalists. Gray's strategic solution – revealed in his copious notes on the parliamentary commissions investigating the affairs of the BM – embodied competence in institutional discourse and set the stage for the supposed ‘cynical’ definition of species later adopted in Darwin's Origin of Species, namely that species are merely what competent naturalists say they are.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.021 | 0.088 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".