Sir Thomas White's Dream: St. John's College, Oxford, The Merchant Taylors' Company, London, and the Reformation
Bibliographic record
Abstract
This dissertation is a dual case study of two institutions, St. John’s College, Oxford and the Merchant Taylors’ Company, London. It considers how these two groups navigated the later Reformation in England, from the accession of Elizabeth I until the beginnings of the English Civil Wars. Although separate corporations, they were tied together by Sir Thomas White, a Merchant Taylor who founded St. John’s College, Oxford. He joined the two establishments in his will, granting the Merchant Taylors’ Company the right to elect up to forty-two of the College’s fifty fellows. This arrangement ultimately resulted in power struggles between the two groups, as each side attempted to press its own interests. These tensions were exacerbated by confessional differences between the two groups. Sir Thomas White originally founded St. John’s College as a training college for Catholic priests during the re-establishment of traditional religion during the reign of Mary Tudor. Following the establishment of the Elizabethan Protestant settlement, it became a haven for crypto-papists and conforming Catholics. St. John’s College remained a religiously conservative institution until the 1580’s, when it became a breeding ground for avant-garde conformity, and later the birth place of Laudianism. Conversely, godly Protestants from the 1570’s onward increasingly populated the leadership of the Merchant Taylors’ Company. Godly members of the London community governed the Company from the turn of the seventeenth century onwards, but more moderate Calvinists who supported the Jacobean and Carolinian courts tempered them. These religious differences led to friction between St. John’s College and the Merchant Taylors’ Company that exploded regularly at the elections of scholars held on St. Barnabas’ day each year. Previous historians have highlighted the financial context of these rows, but have largely ignored the religious and social foundations of tensions between the two groups. This dissertation seeks to rectify this oversight and contributes to the work of early modern English history, Reformation history, social history, and local history. My primary sources include correspondence, official registers, election ballots, state papers, college accounts, company court records, common place books, printed materials, wills, tombs, and material culture.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".