Bibliographic record
Abstract
Graphing the Poetess Susan Brown (bio) The poetess, Tricia Lootens argues, "flickers between history and myth" (1). As she and Florence Boos, among others, argue, this figure operates in intimate relationship to powerful taxonomies, such as race, class, ability, nation, and professional status, that bring home its political significance. I here graph nineteenth-century women writers who figure as poetesses to explore categories associated with this contested figure. These visualizations show a wider array than usual of poetesses—Harriet Martineau sits alongside Felicia Hemans—and cultural affiliations. The politics of poetess figures themselves are likewise diverse, reflecting generational shifts associated with the emergence of public feminism. Digital literary history requires taxonomies.1 I here explore data from the Orlando Project's semantically encoded profiles of more than twelve hundred women writers. Its flagship publication, Orlando: Women's Writing in the British Isles from the Beginnings to the Present (Brown et al. [2022]), embeds in its title the contradiction at the heart of feminist category work: the project aims to trouble and interrogate a category, woman, that it necessarily invokes. The poetess as a closely related category does significant political work. Shifts in the term's prevalence in print culture can be tracked through relative word frequency within the thirteen million books and periodical volumes of the Hathi Trust Digital Library (fig. 1) (Bookworm). This long view of the poetess confirms her discursive rise from the 1780s, but the [End Page 194] Click for larger view View full resolution Fig 1. Relative occurrence of "poetess" within the Hathi Trust Digital Library corpus. connotations of the word are elusive. Mentions from 1760, for instance, come from men's writing and from periodicals.2 The 1846 peak references originate in advertisements, biographies—including Phyllis Wheatley's in Intelligent Negroes—literary publications, grammars, anthologies, and collections by Mary Russell Mitford and others. Results for 2010 come from myriad sources on topics including art, the Middle East, women's writing, movies, and wine. Such associations are evocative, but reading the politics of the poetess as a social, historical, and critical phenomenon more fully requires category work.3 Orlando's biocritical profiles and contextual information are structured and interlinked through categories embodied in semantic markup or encoding. Alison Booth argues that such digital prosopography—driven by typologies or categories—enables intersectional feminist readings at mid-range, between close and distant. Semantic encoding embeds category work within readable text, allowing cross-profile analysis while retaining context and keeping individual poetesses in view. Results facets place poetesses in contexts such as nationality (affiliations include Spanish, German, and Mohawk, challenging preconceptions), the genres in which they wrote (including the epic), historical period, and tags such as "reception" that point to poetesses as critical constructions. Orlando makes its category work explicit in its interface and in the source markup of individual profiles. Orlando's markup both drives the publication interface and engenders structured data that supports inquiry into patterns of the poetess that push against individualist models of literary history. Linked data "triples" in the [End Page 195] form of subject-predicate-object can be visualized as graphs that show predicates or relationships as lines between writers, or between writers and things such as books or concepts. Orlando's data seen this way drives home the complexity of the poetess. Focusing, for instance, on relationships categorized as intertextuality among nineteenth-century authors associated, by themselves or others, with the poetess displays a dispersed set of interrelationships (fig. 2), including many authors not typically regarded as part of the poetess tradition.4 Many challenges surround the use of data for historical inquiry, especially when the data itself is uneven in coverage, as for women writers, and when one is trying to investigate change over time. However, we can begin looking for patterns by grouping Orlando's poetess-identified writers into two groups, those born 1780–1799 and those born 1800–1819; these authors' careers together cover the rise and peak of poetess discourse. The dots in these visualizations represent not only people but also places, organizations, and concepts such as religions, and social identities; lines joining them represent different types of relationships. In the graph of writers born 1780–1799...
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".