Historical Perspectives on Stuttering Treatment: Dean Williams
Bibliographic record
Abstract
You have accessContemporary Issues in Communication Science and DisordersArticle1 Oct 2000Historical Perspectives on Stuttering Treatment: Dean Williams Robert W. Quesal, , J. Scott Yarus, , , , , , and Robert W. Quesal Google Scholar More articles by this author , , J. Scott Yarus Google Scholar More articles by this author , , Google Scholar More articles by this author , , Google Scholar More articles by this author , , and Google Scholar More articles by this author https://doi.org/10.1044/cicsd_27_F_178 SectionsAboutPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In FiguresReferencesRelatedDetailsCited by Journal of Fluency Disorders73 (105927)1 Sep 2022Book ReviewMichael P. Boyle Journal of Communication Disorders (106242)1 Jun 2022Stuttering interventions for children, adolescents, and adults: a systematic review as a part of clinical guidelinesAuli Laiho, Heli Elovaara, Kirsi Kaisamatti, Katri Luhtalampi, Liisa Talaskivi, Salla Pohja, Karin Routamo-Jaatela and Elsa Vuorio Journal of Fluency Disorders60 (1-10)1 Jun 2019An exploratory factor analysis of action responses to stuttering anticipationEric S. Jackson, Naomi H. Rodgers and Derek B. Rodgers Telemedicine and e-Health25:5 (359-368)1 May 2019Telepractice Treatment of Stuttering: A Systematic ReviewMegann McGill, Natasha Noureal and Jordan Siegel Contemporary Issues in Communication Science and Disorders31:Fall (215-224)1 Oct 2004Social Identity and the Stuttering ExperienceFran Hagstrom and Derek E. Daniels Volume 27Issue FallOctober 2000Pages: 178-187 Get Permissions Add to your Mendeley library History Published in issue: Oct 1, 2000 Metrics Topicsasha-article-typesleader-topicsCopyright & PermissionsCopyright © 2000 American Speech-Language-Hearing AssociationPDF downloadLoading ...
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.012 | 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".