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Record W7046948833

Dysfluent in Fiction

2025· other· en· W7046948833 on OpenAlexaboutno aff

Bibliographic record

VenueOAPEN (The OAPEN Foundation) · 2025
Typeother
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsArticulation (sociology)NarrativeDisability studiesNormativeDenialMetisAgency (philosophy)Trilogy
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.993
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.5240.332

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.

Opus teacher head0.011
GPT teacher head0.263
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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