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Record W3135492103 · doi:10.1386/jivs_00027_7

‘Visualizing dysfluency’: An interview with Conor Foran

2020· article· en· W3135492103 on OpenAlexaff
Conor Foran, Maria Stuart, Daniel Martín

Bibliographic record

VenueJournal of Interdisciplinary Voice Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicStuttering Research and Treatment
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSet (abstract data type)Embodied cognitionTypefaceVisual artsArtComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

In 2018, designer Conor Foran presented his work on Dysfluent Mono, a typeface that represents the vocal repetitions of stammered speech, at the symposium Metaphoric Stammers and Embodied Speakers at University College Dublin, Ireland. In 2020, Foran self-published the first issue of Dysfluent Magazine , a magazine about the positive experiences of people who stutter that is set exclusively in Dysfluent Mono. Maria Stuart and Daniel Martin had the occasion to interview Foran on his work in both typography and advocacy for people who stutter.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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.

Opus teacher head0.197
GPT teacher head0.482
Teacher spread0.285 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2020
Admission routes1
Has abstractyes

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