From Written Record to Bureaucratic Mind: Imagining a Criminal Record*
Bibliographic record
Abstract
Abstract In 1518 the chief justice of King’s Bench initiated an attempt to track successful claims of benefit of clergy on the assize circuits to ensure that laymen could make such claims only once, as mandated by a statute dating from 1490. By doing so he was the first to attempt to create a criminal record in England, where an individual felon’s crimes were recorded with the expectation that an earlier crime would have implications for the punishment of a subsequent one. Both this attempt and a later statutory attempt in 1543 were largely unsuccessful, however. They failed, not because of principled opposition or even inertia, but because the well-established bureaucratic structures of the early Tudor period struggled to keep up with the bureaucratic imagination of those who sought to reform or extend the reach of government. The failed attempt to construct a criminal record demonstrates that as the development of print changed information cultures, and the policies of the Tudors led to an intensification of governance, legal records remained profoundly limited by the intellectual and administrative structures within which they operated. Masters of the gathering of information, Tudor governors struggled to adapt old documents to new purposes or to manage information dynamically.
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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.102 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".