Bibliographic record
Abstract
Shakespeare’s play, The Taming of the Shrew, has faced harsh criticism for its sexist portrayal of women and depictions of abuse. Yet, modern adaptations of the play continue to be produced. Gil Junger’s 1999 teen romantic comedy adaptation of The Taming of the Shrew, titled 10 Things I Hate About You, appears to challenge the play’s problematic themes by developing the relationship between sisters Katherine and Bianca beyond the play’s strict, sexist notion that the ideal woman should be obedient and submissive to their husband. In doing so, the film enfranchises the sisters beyond the play’s binary characterization of women as good or bad. Instead turning them into more complex and human characters. Though the film also introduces Kat and Bianca as rebellious and obedient respectively, scenes in which the sisters discuss their romantic relationships as well as address and resolve their own conflicts allow them complex character development as both women and sisters. As such, the film subverts the play’s gender binaries by prioritizing the development of a loving relationship between sisters in favour of heterosexual romance, thus suggesting that sisterhood is a theme worth contemplation and exploration. The characterizations of Kat and Bianca in 10 Things I Hate About You encourages its audience to reject sexist and limiting understandings of women as depicted in The Taming of the Shrew by illustrating the complexities of young women and idealizing the support and love found within sisterhood.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.023 |
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".