Bibliographic record
Abstract
Can the methods of digital, quantitative analysis today be made to communicate with earlier eras of quantitative history? This thesis isolates one database -the Old Bailey Online, a massive collection of published proceedings from one of London, England's busiest court houses -and tests ways in which I can, and cannot, analyze its data to make a meaningful comparison with the quantitative analysis the legal historian John Beattie performed in the 1980s on records pertaining to Surrey and Sussex. In this thesis I am concerned with what we learn from this process of interrogating two different data sets and quantitative methodologies. With certain caveats, I find that a quantitative approach to the Old Bailey records does not generate findings for London that are significantly different than Beattie's for Surrey and Sussex. Even if my current results are to accept the null hypothesis, the importance of "zero" in this case becomes that we now know where not to focus our research -not on looking for statistical difference in crime between these two areas in this period and, instead, perhaps focusing on qualitative data regarding the people who experienced and had ideas about crime in this historical context.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".