Bibliographic record
Abstract
In Dysfluent in Fiction, Riley McGuire unspools a literary history of vocal disability in the nineteenth century, arguing that this underexamined literary trope helps us to understand vocal hierarchies that still structure our present. Adopting the term “dysfluency” to show departure from normative expectations of pace, pitch, and fluency, McGuire reveals how dysfluent speech populates an enormous number of nineteenth-century texts and played a formative role in the lives of some of the period’s most influential writers. Dysfluent in Fiction examines anglophone literature during the long nineteenth century in both England and America by authors such as William Makepeace Thackeray, Charlotte Brontë, Lewis Carroll, Mary Elizabeth Braddon, and Frederick Douglass. Examples of dysfluencies across genres include lisping lovers, a baby-talking fairy, a mute detective, various disabilities in narratives of enslavement, and more. These representations show how disabled speech was both stigmatized and celebrated in ways that clarify our contemporary response to the spectrum of human articulation and that are a vocal corollary to current notions of neurodiversity. Dysfluency’s power, McGuire contends, lies in its denial that a single mode of articulation is possible, let alone desirable.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".