"The Limits of the Imaginable": Women Writers' Networks during the Long Nineteenth Century
Bibliographic record
Abstract
The rapid rise of the digital humanities over the past decade has transformed literary study, helping scholars to discern broader patterns in print culture and media history. Engaging with methodologies such as data mining, macroanalysis, and network analysis, my Master's Essay utilizes these computational analytics approaches in order to address longstanding critical questions in women's literary history - in particular, how might these tools help us understand the crucial rise of women's networks during the long nineteenth century. To what extent were women's relationship s with fellow female authors important to their success in a male-dominated publishing marketplace, and what new insights are gained from viewing these relationships on a macro-analytic level, rather than simply viewing the individual network or the network of "important" or "canonical" writers associated with a particular literary period or movement?\nIn taking a distant approach without privileging canonical authors over others, the macro-network I generated from mining bio-data of nearly 700 women writers becomes a fluid model from which new trails of scholarship can be mapped rather than a stagnant source of evidentiary support for pre-existing arguments. Utilizing networking software to track details of women's interactions with one another my essay reveals several surprising who functioned as crucial nodes in communities of women writers during the long nineteenth century - Joanna Baillie, Geraldine Jewsbury, and Margaret "Storm" Jameson - and offers an analysis of why this high connectivity has not translated into canonicity.\nOf course, any database of women writers obscures as much as it reveals about women's experience as participants in gendered publication networks, and my essay closes with analysis and acknowledgement of the archival absences, gaps, and biases of human politics embedded in the data in- and exclusion process encoded within the construction of the Cambridge University's Orlando Project digital database, from which my information was mined.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".