Bibliographic record
Abstract
This chapter explores Henry Dresser's establishment in scientific society during the 1860s, when he was in his twenties. Through the 1860s, he established himself within the leading group of ornithologists in Britain, as a friend, a correspondent, as a competent ornithologist and as a collector. He valued his acquaintances with other naturalists greatly and collected signed letters and photographs from them from the mid-1860s onwards, which he kept in an album. Many natural history societies published the names and addresses of their members in their journals (including the Ibis produced by the British Ornithologists' Union), in order to promote communication among them. Dresser's own correspondence network increased with every one of his travels, adding new people to his network for exchanging information and specimens. By the end of 1864 his correspondence network included ornithologists in America, Canada, Prussia, Finland and Sweden, as well as the leading ornithologists in Britain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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; a candidate call from one teacher head, 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".