Feminist Markup and Meaningful Text Analysis in Digital Literary Archives
Bibliographic record
Abstract
In this research paper, I examine three digital archives of women writers--University of Nebraska-Lincoln’s Willa Cather Archive, Northeastern University’s Women Writers Online, and University of Alberta’s Orlando Project--for evidence of encoding practices and computational text analysis experimentation that supports feminist scholarship. I provide a brief overview of text encoding practices and controversies in digital literary studies, emphasizing research that suggests heavily detailed and interpretative markup results in more meaningful text analysis outcomes. I situate feminist text encoding and analysis practices and technologies within a larger argument for the use of detailed, interpretative and critical markup. I begin my research on the premise of Jacqueline Wernimont’s assertion that text encoding and analysis practices and technologies are political tools that open a space for feminist intervention that can support feminist literary scholarship and reveal the integral place of women’s writing within the field of digital literary studies. After examining the three digital archives as well as any documentation of their markup and text analysis practices, I determine that feminist markup--tagsets specifically designed to support feminist inquiry--exists on a spectrum and that highly detailed and interpretative feminist markup leads to more meaningful text analysis outcomes for feminist scholarship, revealing complex social, cultural and political data pertaining to gender and literary history. I conclude that feminist-specific markup is important and necessary to support feminist scholarship, and that detailed and interpretative markup can be leveraged to produce more meaningful and critical text analysis results in other areas of digital literary inquiry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".