‘Thys my poore labor to present’: Mary Bassett's Translation of Eusebius's Ecclesiastical History
Bibliographic record
Abstract
: Chapter 1 considers Mary Bassett's translation of Eusebius's Ecclesiastical History (BL MS Harley 1860) as a manuscript dedicated to Princes Mary Tudor and intended for circulation among a circle of English Catholic readers. The chapter contextualizes Bassett's translation within the More family's educational tradition and legacy of subversive writing and translation. Bassett's lexical choices reveal her articulation of affinity with a religious and political community. I suggest that these, coupled with the terms of the dedicatory letter to Mary, reveal Bassett's translation of this highly politicized patristic text to be a work that deliberately creates a politicized subject for the view of her readers. Keywords : dedications; Eusebius; Mary Bassett; Mary I; patronage; translation Mary Bassett (c. 1522–1572) is perhaps best known as the granddaughter of Thomas More and the translator of his De Tristitia Christi , printed in 1557 in More's collected English writings. Her translation of the first five books of Eusebius's Ecclesiastical History from Greek into English and Latin is less well known, in part because of its manuscript form, dedicated to the Princess Mary (later Mary I) and now held at the British Library. The Harley catalogue records that the now leather-bound copy of Bassett’s Ecclesiastical History was at one time ‘bound in a Cover of Purple Velvet, Gilt on the Edges, &c. Seemeth to have been the Present-Book to the above mentioned Princess’. Evidence that the manuscript was actually given to Mary is inconclusive, but the elaborate binding and visual presentation of the text itself (with embellished capitals and neatly ruled margins in red) suggests that this was the copy destined for her. Bassett's gift includes a long and detailed dedicatory letter in which she reflects on her methodology and theoretical stance in making the translation. Bassett's voice emerges clearly and confidently from within the conventions of the dedicatory letter and this chapter explores the ways in which her translation and its circulation as a gift to the princess Mary Tudor offered Bassett an opportunity to declare publicly her religious and political affiliations to this controversial figure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".