Chapter 12</br>Evaluating digital remediations of women's manuscripts
Bibliographic record
Abstract
In this chapter, we assess how existing digital projects that feature women's manuscripts (c. 1550-1900) can aid research on literature, history, and cultural studies. We argue that the best digital remediations of women's manuscripts contribute, paradoxically, both to their hypermediacy (those elements that remind users they are not faced with a manuscript) and their immediacy (those aspects that hide the remediation and encourage users not to reflect on the medium). We showcase the range of scholarly engagement possible through a variety of sites, including British Literary Manuscripts Online, Perdita Manuscripts, and Jane Austen's Fiction Manuscripts. Analyzing these resources demonstrates how they can best be used by teachers and scholars. By evaluating digital remediations of women's manuscripts, we highlight best practices for manuscript digitization and point to new directions for digital projects and literary study. Dans ce chapitre, nous évaluons comment les projets numériques qui présentent les manuscrits des femmes (vers 1550-1900) peuvent aider la recherche en littérature, en histoire et en études culturelles. Nous soutenons que les meilleures remédiations numériques de manuscrits de femmes contribuent paradoxalement à la fois à leur hypermédialité (ces éléments qui rappellent à l’utilisateur qu’il n’est pas confronté à un manuscrit) et leur instantanéité (ces aspects qui dissimulent la remédiation et encouragent l’utilisateur à ne pas songer au médium). Nous présentons la gamme d’engagements érudits possibles par l’entremise de plusieurs sites, incluant British Literary Manuscripts Online (Manuscrits littéraires britanniques en ligne), Perdita Manuscripts, et Jane Austen’s Fiction Manuscripts. L’analyse de ces ressources démontre comment celles-ci peuvent être le mieux utilisées par les professeur(e)s et les universitaires. En évaluant les remédiations numériques des manuscrits de femmes, nous mettons en évidence les meilleures pratiques pour la numérisation de manuscrits et ouvrons la voie à de nouvelles orientations pour les projets numériques et les études littéraires.
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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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".