Bibliographic record
Abstract
There were 120 or so settlements in Domesday Book that can be categorized as in some sense urban. Fifteen are called cities ( civitas ), the remainder, either directly or by implication, boroughs ( burgus ). Civitas translates OE cæster , ‘city, walled town, fortification’, and refers to former Roman settlements. Burgus , representing OE burh , ‘stronghold’, had less specific connotations. There was many a borough, even major ones like Derby, that had never been defended. By the eleventh century burgus had come to mean something like the modern ‘town’, and as such embraced all settlements of the kind, including cities. A vibrant urban economy had long characterized English society. York, Lincoln, Norwich, London, Winchester had been major urban centres of an international stature from at least the early tenth century. Many smaller county boroughs had flourished as local and regional markets from the same period. Archaeological excavations in the last forty years have demonstrated just how diversified was their industrial base and how wide were their trading contacts. The boroughs that the Normans found in 1066 were rich, complex settlements with a long history of urban life. It is now realized that their origins and patterns of development were various. In the past most thinking about towns had been reductionist. From the nineteenth century attempts were made to define urban status as if it was a legalistic quantity founded in charters of liberties or the like. The work of Ballard, himself a town clerk, epitomizes the approach of historians who were steeped in Victorian notions of rational town government. But not all accounts were so simplistic. Maitland was not immune to the intellectual climate of the time, but his account of boroughs, in Domesday Book and Beyond and his study of Cambridge in Township and Borough , was by far the most nuanced. He recognized that boroughs were settlements that retained rural characteristics; most had fieldsand agriculture was still a significant activity for townsmen in the eleventh century. Nevertheless, he accepted that they had a special status that distinguished them, both legally and functionally, from the surrounding countryside.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.175 | 0.032 |
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".