Bibliographic record
Abstract
The one thing that can be said about London history is that it has staying power. There was a time in the 1970s when community studies were all the rage, when a concern for region, province, and even pays threatened to cast English, and perhaps even British history in a more Annalesiste mould, at the very least to highlight the divergent histories of the island. Within this prospectus London threatened to lose some of its supremacy as a premier site for historical investigation and as an exemplary site for historical trends. But times have changed. Although there continue to be some good micro-histories of communities, local history seemed to thrive best when social-science-inflected history was to the fore. With the shift to cultural history, to post-colonial history, to the histories of alternative sexualities or of consumerism, Cobbett’s “Great Wen” has made a comeback. In the latest clutter of metropolitan histories stand these three books. Two of them, those of Professors Hitchcock and Shoemaker, flow out of a larger project in which they have been engaged, the Old Bailey Proceedings online. This is a massive digitalization of the printed proceedings of London’s central criminal court from 1674 to 1834, comprising over 100,000 trials. From any part of the internet, researchers can call up the names, places, crimes, canting review essay / note critique
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".