Contextualizing Canonical Inclusion: The Case of Early Modern English Female-Authored Non-Canonical Verse
Bibliographic record
Abstract
The Early Modern English female-authored text is recently (to be specific since 2000s) introduced to the English literary canons as supplementary material to anthologized literary lists such as the Norton Anthology of English Literature. This recent shift in their inclusion is argued as insufficient to the abundantly abandoned English female-authored publications printed between 1450s until the early 1700s. The process of their inclusion in the literary anthologies of the English Renaissance is seen as integral towards building an equitable representation of this age. Offering an equitable inclusion of women’s literature in this era is the aim of this study. This paper will first offer an in-depth contextualization to the canonization of female-authored texts with focus mainly on their exclusion from literary canons. Then, the study offers methodized canonized inclusions of Early Modern female-authored texts. In conclusion, the paper provides a detailed sample of a course plan that aims at the inclusion of female-authors during this era in a general mandatory course (for undergraduate students at the School of Foreign Languages at the University of Jordan) titled “English Literature from the Beginning until 1660s.” This course plan has been prepared and revised by the researchers from the years 2019-2023 to ensure sampling the inclusion of female-authored texts in this survey course.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".