Estate Management, Maps and Map-making in Oxford and Cambridge 1580-1640*
Bibliographic record
Abstract
Abstract What part did the universities and colleges of Oxford and Cambridge play in the early years of making manuscript estate maps in England? These colleges formed a group of educated landowners, who participated in the lively sixteenth-century land market and added former monastic lands to their holdings. They held estates across England and Wales and land was a vital source of income: the extent of a college ‘s landholdings was an indication of its wealth or poverty. Besides being major landowners, the colleges of Oxford and Cambridge might be expected to have contributed to carto graphic developments as they were the places in the country that provided the highest level of education, where there was considerable interest in mathematics, geography and maps. It is thus instructive to examine the extent to which the colleges patronized land surveyors and commissioned maps, the uses to which plans were put, and instances when maps were not drawn. Comparison of the two universities enables each to be seen in a wider context. Many men in sixteenth- and early seventeenth-century Cambridge owned maps. One was Laurence Hitchcock BA, pensioner of Trinity Hall, Cambridge, who died in 1573 and left ‘a mappe of the whole world ‘ and four other maps together with five legal works and those of Cicero and Sallust. ‘ Hitchcock was one of those book owners whose probate inventories were presented in the Vice-Chancellor ‘s court between 1535 and 1601, over one-quarter of whom owned maps. About three-quarters of these people owned one or two maps; under one-quarter owned more than four. Among the map-owners were booksellers, clergymen and junior members, but the majority (77 per cent) were fellows or masters of colleges, or incumbents of Regius chairs. Some owned maps to help in particular studies; others used their maps as a form of decoration .
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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 | 0.000 | 0.000 |
| 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".