Characteristics and Lasting Contributions of 19th-Century American Neurologists
Bibliographic record
Abstract
This project sought to identify characteristics and lasting contributions of 19th-century members of the American Neurological Association (ANA). Members were categorized by elite status, citation frequency, founder or charter member, elected to honorary membership, published a monograph on a neurologic or psychiatric topic, born in the United States or Canada, and received any medical training outside the United States or Canada. Citations to 19th-century publications in Science Citation Index were analyzed for the period 1974-1995. ANA membership was restrictive, but membership nevertheless increased dramatically in the first 25 years from its founding in 1875. 19th-century ANA members frequently served in a leadership capacity within the organization, published neurologic or psychiatric monographs, and received medical training abroad. Highly cited members were more likely to be instrumental in founding and developing the organization, and were likely to be recognized by their contemporaries as eminent. 19th-century ANA members made significant and lasting contributions in many areas of neurology and psychiatry. Articles with lasting relevance were early descriptions, points of comparison, and controversial articles.
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 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".