Bibliographic record
Abstract
Fictional representations of slavery begin with texts such as Aphra Behn’s Oroonoko; or, The Royal Slave , a novella that was hugely popular in its time and widely adapted for the stage for over a century after its publication. The most recent adaptation, Biyi Bandele’s 1991 play, is an adaptation of adaptations. The Royal Shakespeare Company enlisted Nigerian-born Bandele to adapt a play based on both Restoration dramatist Thomas Southerne’s 1695 Oroonoko and John Hawkesworth’s 1759 play by the same name. These multiple layers of adaptation are no less rich than the sources Behn used for her novella. Depicting the tragedy of an African prince who, along with his beloved, is sold into New World slavery, Behn follows the slave trade back across the Atlantic to Africa, becoming the first English author to represent sub-Saharan African people in their own continent. To familiarize her readers with her characters and settings, Behn made use of the conventions of the New World travel story, the courtly romance and the heroic tragedy and, especially, the conventions of the Oriental romance, including the trope of the Noble Savage. Beyond familiarizing the foreign, Behn’s strategies helped highlight the human tragedy of the slave trade. As a heroic tragedy, Behn’s novella ‘exaggerates precisely those emotional experiences that were often suppressed by historical description, debates, and documentary records’ about the slave trade. In its various adaptations, Oroonoko became part of the abolitionist movement in England, and was eventually considered a forerunner to Harriet Beecher Stowe’s Uncle Tom’s Cabin (1852).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".