The Master of the Royal Mint: How Much Money did Isaac Newton Save Britain?
Bibliographic record
Abstract
Summary From the extant statistical data, the paper reconstructs several episodes in the history of the Royal Mint during Isaac Newton’s tenure. We discuss four types of uncertainty that are embedded in the production of coins, extending Stephen Stigler’s work in several directions. The jury verdicts in trials of the pyx for 1696–1727 allow judgement on the impartiality of the jury at the trials. The verdicts, together with several remarks by Newton in his correspondence with the Treasury, allow us to estimate the standard deviation σ in weights of individual guineas coined before and during Newton’s Mastership. This parameter, in turn, permits us to estimate the amount of money that Newton saved Britain after he put a stop to the illegal practice by goldsmiths and bankers of culling heavy guineas from circulation and recoining them to their advantage; a conservative estimate of savings to the Crown is £41510, and possibly three times as much. The procedure by which Newton probably improved coinage gives historical insight on how important statistical notions—standard deviation and sampling—came to the forefront in practical matters: the former as a measure of variation of weights of coins, and the latter as a test of several coins to evaluate the quality of the entire population. Newton can be credited with the formal introduction of testing a small sample of coins, a pound in weight, in the trials of the pyx from 1707 onwards, effectively reducing the size of admissible error. Even Newton’s ‘cooling law’ could have been contrived for the purpose of reducing variation in the weight of coins during initial stages of the minting process. Three open questions are posed in the conclusion.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".