Discourse on History, Geography, and Law: John Dee and the Limits of the British Empire, 1576-80
Bibliographic record
Abstract
Using history, geography, and law as his supporting evidence, the English polymath John Dee (1527-1608) helped the British crown to define and defend the limits of its empire. In a series of works prepared for Queen Elizabeth and her senior advisors between 1576 and 1580, Dee argued for the existence and recovery of a vast British Empire. He based these arguments principally on the nascent precepts of international law. Dee proclaimed the queen’s right to trade in new found lands by natural law, and to draw into her dominion those lands that were discovered by English subjects and were not currently in the actual possession of a Christian prince by civil law, canon law, and the law of nations. He laid the foundation for the British claiming territory by effective occupation rather than mere discovery. He also challenged Spain’s and Portugal’s claims of dominion by taking on the explicit language of Alexander VI’s papal bull awarding all terra incognita to the Iberian countries. Although they were developed within specific contexts, Dee’s arguments were well-enough grounded in scholarship and sufficiently influential to germinate into long-term intellectual justifications for claiming sovereignty. These arguments came to be used by the crown in letters patent and official memoranda, and in disputes with other European nations who challenged England’s title to overseas territories.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| 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".