Editor’s Preface and Acknowledgments
Bibliographic record
Abstract
at the University of York on 'York 1190: Jews and Others in the Wake of Massacre'.We would like to thank Dr Hannah Meyer (Cambridge) and Dr Hugh Doherty (Oxford), who joined us in 2009 in planning the conference.Their influence can still be detected in several of the chapters.The conference was supported financially by the British Academy.Generous further assistance was provided by The Jewish Historical Society of England (both the London and the Leeds branches), the Royal Historical Society and the Departments of English and History as well as the Centre for Medieval Studies at the University of York.Following the conference, the Leeds branch of the JHSE established an annual prize for student work on the history of Jews in Yorkshire (in any period).We would particularly like to thank Nigel Grizzard and Malcolm Saunders for their advice in developing the conference and ensuring that we could provide excellent hospitality for all our guests.The King's Manor and the hospitium of St Mary's Abbey proved ideal venues, while English Heritage provided access to Clifford's Tower with excellent stewardship for our groups.Under the direction of Professor Elisabeth Dutton (Fribourg), the Centre for Medieval Studies' own 'Lords of Misrule' enriched the event with their performance of the medieval Croxton Play of the Sacrament and Steven Berkoff's Ritual in Blood.Every chapter in this volume owes a debt to Professor Barrie Dobson for his singular contribution to writing the history of the massacre and of medieval
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.253 | 0.141 |
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".