Mapping Out the Monstrosity of Disabled Bodies in Hunt-Kennedy’s Between Fitness and Death (2020) and Nicholas’ Canadian Carnival Freaks and the Extraordinary Body (2018)
Bibliographic record
Abstract
This comparative book review explores the concept of monstrosity in Hunt-Kennedy’s Between Fitness and Death (2020) and Nicholas’ Canadian Carnival Freaks and the Extraordinary Body (2018). This analysis contrasts how Black bodies, seen as congenitally disabled and ‘enfreaked’ bodies exhibited in sideshows, are discursively framed in monstrous terms. This conception began at birth, continuing to their eventual staging and display upon auction blocks (the enslaved) or sideshow stages (the enfreaked). This review is organized as follows: a brief and general summation of both texts, a discussion of the thematic similarities between them, and a summary of what each text brings to our understanding of disability. Keywords: Slavery, disability, sideshows, freaks
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".