Introduction: Cultural Crossroads and Medical Learning in the Medieval North Atlantic World
Bibliographic record
Abstract
Cultural Crossroads and Medical Learning in the Medieval North Atlantic WorldThis volume explores aspects of medicine and medical ideas in the medieval North Atlantic world, focusing primarily on vernacular texts and traditions from Ireland, England, Wales, and Scandinavia across a chronological range that spans the early to late Middle Ages.On the one hand, the book is a collective effort on the part of scholars of medieval medicine and related fields to respond to a wealth of recent scholarship on the transmission and translation of texts from various genres -particularly historical and literary ones -across the frequently shifting linguistic, political, and national boundaries that have shaped this archipelagic region throughout its history. 1 The volume also seeks to transcend disciplinary boundaries within the field of medieval medicine itself, however, by recognizing that there is much to be gained from fostering dialogue among scholars working on medical texts or themes in the various linguistic traditions of the Insular world.In the area of medieval medicine, where the focus of so much valuable work has often been directed further south towards Mediterranean Europe and the medieval Islamicate cultures of Spain, North Africa and the Near 1 Some notable recent examples include Allport and others, eds, Networks in the Medieval North; Brady, The Origin Legends; Busby, French in Medieval Ireland; Byrne and Flood, eds, Crossing Borders; Edmonds, Gaelic Influence; Eriksen
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".