Bibliographic record
Abstract
Human variation has always existed, though it has been conceived of and responded to variably. Beholding Disability in Renaissance England interprets sixteenth- and seventeenth-century literature to explore the fraught distinctiveness of human bodyminds and the deliberate ways they were constructed in early modernity as able, and not. Hobgood examines early modern disability, ableism, and disability gain, purposefully employing these contemporary concepts to make clear how disability has historically been disavowed—and avowed too. Thus, this book models how modern ideas and terms make the weight of the past more visible as it marks the present, and cultivates dialogue in which early modern and contemporary theoretical models are mutually informative. Beholding Disability also uncovers crucial counterdiscourses circulating in the English Renaissance that opposed cultural fantasies of ability and had a keen sensibility toward non-normative embodiments. Hobgood reads impairments as varied as epilepsy, stuttering, disfigurement, deafness, chronic pain, blindness, and castration in order to understand not just powerful fictions of ability present during the Renaissance but also the somewhat paradoxical, surprising ways these ableist ideals provided creative fodder for many Renaissance writers and thinkers. Ultimately, Beholding Disability asks us to reconsider what we think we know about being human both in early modernity, and today.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.952 | 0.939 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".