BEWARE THE BEARDED WOMAN: FREAKS, THE FEMALE BODY, AND NON-RECOGNITION
Bibliographic record
Abstract
This thesis explores the concept of recognizable and non-recognizable bodies through examining representations of female freaks in literature.Bodies must be recognizable in order to be considered human, yet the non-recognizable body or freakish body offers alternate possibilities of embodiment that are usually overlooked.This thesis focuses on what happens when the non-recognizable female freak is in the public gaze and the gaze among intimates.Both of these spaces are ambivalent spaces for the female freak in which she finds simultaneous danger and power, identification and manipulation.In these texts the public space is one in which the female freak may find both economic and erotic power, yet allowing her body to been seen puts her in danger from the seer.The female freak cannot control the gaze of the audience, which can lead to her body being objectified, or risk physical harm.In more intimate spaces in which the female freak is among family and friends, her body may be recognizable because she is with people who are similarly embodied, but she may also be in danger from intimates who wish to dictate what she can and cannot do with her body.The intimate space is also the space in which the female freak can shape her own body and define her own aesthetic and personal erotic.The literary texts analyzed in this thesis present the freakish female body in a new light, and ask the reader to question her definitions of freakish and normal.The texts suggest alternate ways of being embodied and of looking at bodies that are considered non-recognizable, suggesting that we need to conceive of new ways of conceptualizing bodily difference.
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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 | 0.002 | 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 | 0.007 | 0.038 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".