Notice bibliographique
Résumé
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...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».