Bibliographic record
Abstract
It may initially seem odd that a chapter on books beyond England has been included in a volume of essays apparently dealing with the production of books in England. But English texts and manuscripts travelled widely in both geographical and social terms throughout this period, as, indeed, did their writers and later copyists, readers and hearers. The manuscripts that form the basis of this chapter are part of an under-investigated ‘archipelagic’ literary culture of book production, reception and reading that has been eclipsed by a larger and still imperfectly written English book history – larger, that is, because it deals with a much greater number of extant manuscripts and a greater population of Anglophone readers across a wider geographical area that includes London and Westminster – and imperfectly written (notwithstanding several modern team efforts), precisely because it is so metropolitan in focus. Not unnaturally, this larger version of English book history has gained recent critical attention because of its focus on the production and distribution of works by major English authors, revealed through metropolitan and other strongly regional patterns of consumption in England in the period before printing. Nonetheless, ‘Ireland’, ‘Scotland’ and also ‘Wales’ have always occupied significant places in the Anglophone imagination and in the late medieval ‘English’ border cultures that manifested themselves as peripheral regional presences across these islands.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.175 | 0.034 |
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".