Tacit Narratives in the Manuscript Collections of Matthew Parker and Robert Cotton
Bibliographic record
Abstract
Over the past two decades, the history of early modern archives has been a topic of considerable interest among historians, and their research has drawn attention to the complex motives and commitments that inspired individuals, communities, and institutions to create, collect, preserve, and use archives in the early modern period. Their research also offers insights into what Eric Ketelaar has called the “tacit narratives of power and knowledge” woven into the formation, preservation, and use of archives and opens up new avenues for exploring the social history of archives. The English Protestant Reformation has provided the backdrop for some of this work, highlighting the ways in which post-Reformation libraries functioned as “polemical weapons” in political and religious struggles to control the historical narrative about the roots of the Reformation. The libraries built by the antiquarian collectors Matthew Parker and Robert Cotton in the 16th and 17th centuries furnish useful examples of the kinds of tacit narratives embedded in the selection, preservation, and use of post-Reformation manuscript collections. This article draws on the research undertaken by early modern historians into the collecting and compiling practices underpinning the formation and use of the Parker and Cotton manuscript collections to demonstrate how their work is helping to illuminate the tacit narratives embedded in early modern archives as well as broadening and deepening the social history of archives.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.031 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".