Bibliographic record
Abstract
Our joint presentation combines performance and parody to create a playful and accessible approach to understanding Shakespearean texts and the power of adaptation. We intend to populate the TV show “The Bachelorette” with Shakespeare’s characters, thus using popular culture to reveal the problematic aspects of Shakespeare’s plays. We want to appeal to up and coming generations and include those who may not have the background or elite education to understand Shakespeare on their own. One possibility we hope to explore is Ophelia choosing among several Shakespearean tragic heroes vying for her hand. Additionally, we will deconstruct Shakespearean gender norms and notions of sexuality, and probe the queer experience, or the lack thereof, in Shakespearean adaptations. Many Shakespearean popular media adaptations skirt around the queer undertones of the texts from which they derive their material. Our exploration is facilitated by the critical and parodic nature of our presentation, drawing on influences from Shakespeare’s own works and popular adaptations like Baz Luhrmann’s 1996 film Romeo + Juliet and Iqbal Khan’s 2015 theatrical production of Othello to challenge and subvert Shakespearean conventions. While many Shakespearean adaptations exalt and revere Shakespeare, our experiment hopes to discover what a queer and irreverent eye might make of Shakespeare. Mediums: Presentation Mini essay
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".