Bibliographic record
Abstract
Books reviewed in this article: Cristina La Rocca (ed.), Italy in the Early Middle Ages 476–1000 Mary Dockray‐Miller, Motherhood and Mothering in Anglo‐Saxon England Samantha J. E. Riches and Sarah Salih (eds), Gender and Holiness: Men, Women and Saints in Late Medieval Europe Anthony Goodman, Margery Kempe and her World Bettine Birge, Women, Property, and Confucian Reaction in Sung and Yüan China 960–1368 David Kuchta, The Three‐Piece Suit and Modern Masculinity: England, 1550–1850 Harry Cocks, Nameless Offences: Homosexual Desire in the 19th Century Wendy Rosslyn (ed.), Women and Gender in 18th‐Century Russia Irina Paert, Old Believers, Religious Dissent and Gender in Russia, 1760–1850 Shirley Wilson Logan, ‘We Are Coming’: The Persuasive Discourse of Nineteenth‐Century Black Women Adele Perry, On the Edge of Empire. Gender, Race and the Making of British Columbia 1849–1871 Myra Rutherdale, Women and the White Man's God. Gender and Race in the Canadian Mission Field Maina Chawla Singh, Gender, Religion and ‘Heathen Lands’: American Missionary Women in South Asia (1860s–1940s)
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.615 | 0.620 |
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".