Bibliographic record
Abstract
A scribe or printer undertaking to reproduce a text faced the task of reproducing or supplying features that would help a reader navigate that text. During the period between 1350 and 1500, readers came to expect in an English book several elements that would either facilitate reading or help a reader find particular passages or topics. These elements included headings for parts of a work such as chapters or books (headings which this essay will also refer to sometimes as rubrics or as incipits and explicits); litterae notabiliores or ‘capital’ letters; paraphs; ‘running heads’ at the tops of pages to identify a text or part thereof; various kinds of marginal material which identified topics, speakers, sources translated or authorities cited in the text, which simply highlighted passages of special interest or (less often) which provided direct commentary on the text; and, in more expensive books, borders which, like headings, indicated part-divisions. In an influential essay, M. B. Parkes has shown how the origins and growing popularity of these features related to large cultural changes in the renaissance of scholarly learning in the twelfth and thirteenth centuries, such as new ways of reading, the rise of universities, the production of new kinds of books which compiled material from many sources and the composition of encyclopaedic works. The page came to be designed in ways that clarified the division of a work into parts and also the relationship of several kinds of writing that might appear on the same page.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.185 | 0.124 |
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".