With Time Comes Agency: The Evolving Role of Women over Time as Seen in Much Ado About Nothing, "Of Beren and Lúthien," and Buffy the Vampire Slayer
Bibliographic record
Abstract
In his first-year essay “With Time Comes Agency: The Evolving Role of Women Over Time as Seen in Much Ado about Nothing , ‘Of Beren and Luthien,’ and Buffy the Vampire Slayer, ” Danny Tetlock analyzes not only the way that the role of women has changed over time, but the way that the tropes and conventions writers use to portray women have changed. Drawing on conventional images of women ranging from Scandinavian medieval valkyries to early-twentieth-century suffragettes to popular television heroines such as Xena, Danny focusses on the ways in which William Shakespeare, J.R.R. Tolkien, and Joss Whedon not only reflect the changing images of women across times, but also participate in the change. In the end he argues that art does not merely reflect reality, but that works of fiction can actively change the way society envisions women. Dr. Kathy Cawsey
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".