De Moivre's knowledge community: an analysis of the subscription list to the <i>Miscellanea Analytica</i>
Bibliographic record
Abstract
Abstract In 1730 Abraham De Moivre published Miscellanea Analytica, a book containing research results in several areas of mathematics, but especially in probability theory. Subscribers to the book have been identified from the subscription list and personal information about these individuals has been collected and stored in a database. Once data collection was completed, the data were retrieved, graphed and analysed to look for connections between the subscribers. On the basis of the connecting links that we found, De Moivre's career as a tutor has been partly reconstructed and some of his relationships within the Royal Society have been established. It was found that the heart of De Moivre's knowledge community and support was based on Whig political connections combined with aristocratic family connections. A reconstruction is suggested for how De Moivre was able to develop this knowledge community beginning in about 1689.
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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 | 0.007 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".