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 acceptthe 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.43 Shawn Graham, "Failing Productively in Digital Archaeology," Electric Archaeology, last modified March 14, 2017,
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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 | 0.075 | 0.305 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.018 | 0.044 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".