Bibliographic record
Abstract
Katie Houston and Louise Nash for all their moral support, for keeping me company on the occasional research trip, and in Louise's case, for putting me up (and putting up with me) when I was visiting the archives in Edinburgh.Last, but by no means least, I wish to thank my parents.Not only have they encouraged my love of history since I was little, they have also shown me unending support and unstinting generosity, both in the completion of this book and in everything else I have ever done.Although I am sure I haven't said it as often as I should have, I am forever grateful for this help, for the holidays they surrendered to my research trips and for the times they put up with my grumpiness -verging on a caveperson persona really -when I was in the midst of a bad day.In addition, my Dad has always solved any and all computer problems which have come my way, while drilling into me the mantra 'back-up, back-up, back-up' so that my work is probably better insured against loss than the house!My Mum meanwhile, has always been on hand to proofread sections of text for me when I was too tired to even spell my own name correctly any more, while making sure that I didn't slip into a lifestyle consisting of junk food eaten at the computer and no natural light.
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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.470 | 0.391 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".