“Formed with Curious Skill”: Blessington’s negotiation of the “poetess” in Flowers of Loveliness
Bibliographic record
Abstract
This essay demonstrates how Marguerite, Countess of Blessington, relied upon but also diverged from poetess poetics as a way of forging her own poetic voice without sacrificing the economic success enjoyed by anyone willing to write poetry in the style and manner of the annuals. As a close-reading of both content and context of Blessington’s satiric poem “The Stock in Trade of the Modern Poetess” reveals, Blessington capitalizes on her Byronic disdain for the annuals, thus simultaneously spurning poetess poetry and making a bid for financial success, the success typically enjoyed by writers willing to truck in poetess “stock.” In order to achieve both financial success and poetic merit, Blessington plays to the crowd who expects beautiful images of the nation in poetess poetry, but not by using the same "stock" as does poetess poetry: instead of setting up national heroines to be worshipped, Blessington recasts typical symbols used by the poetess for romantic and domestic relationships as ties that bind one person to another in the nation. Blessington’s oeuvre illustrates one dramatic instance of participation in the poetess tradition, showing us that women writers didn’t simply succumb to poetess conventions but rather manipulated the “stock in trade” of the poetess in order to achieve literary without sacrificing financial success.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".