Bibliographic record
Abstract
Combatting the Literary Canon through Performance Ophelia’s Last Word(s) reexamines the literary canon by combatting the trope of “fair and fragile” women. Written for Dr. Varadharajan’s English 421 course on adapting Shakespeare, this presentation/performance explores the role of women in Hamlet to offer a new voice to the archetypal shadow maiden, Ophelia. Among many concerns, the rap questions why audiences contemplate Hamlet’s madness but presume Ophelia’s to be authentic. We reinforce harmful gender representations when viewing Shakespeare’s women with a traditional and canonical lens. Examining women as codependent on male figures, emotional, and inept is a lens that perpetuates this standard for modern audiences. Adaptation provides an opportunity to reenvision these women and their fate. Using evidence and information omitted from the text, I offer an alternative ending for Ophelia where she could preserve and fight the problematic representation of fairness and fragility. Adaptation is a valuable way to approach inquiry-based learning because it provides the opportunity to reenvision and reinvent canonical norms that sabotage contemporary efforts at inclusion and equality. The canon is widely accepted and taught in the English discipline but primarily speaks to and from white males. It is intimidating to challenge but also crucial. Therefore, inquiry-based learning like this performance is a spirited attempt to challenge and change the way we learn about Shakespeare. We cannot examine women, representation, and inclusion issues until we reexamine the way women appear in canonical English works. Adaptation is key.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".