Bibliographic record
Abstract
No AccessPerspectives on Fluency and Fluency DisordersArticle1 Aug 2007Defining and Measuring Normal Fluency Patrick Finn Patrick Finn University of ArizonaTucson, AZ Google Scholar More articles by this author https://doi.org/10.1044/ffd17.2.14 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In References Adams, M. R. (1982). Fluency, non-fluency, and stuttering in children.Journal of Fluency Disorders, 7, 171–185. Google Scholar Brown, R. (1973). A first language: the early stages.Cambridge, MA: Harvard University Press. Google Scholar Chambers, F. (1997). What do we mean by fluency? System. 25, 535–544. Google Scholar Finn, P. (1997). Adults recovered from stuttering without formal treatment: Perceptual assessment of speech normalcy.Journal of Speech, Language and Hearing Research, 40, 821–831. LinkGoogle Scholar Finn, P. (1996). Establishing the validity of recovery from stuttering without treatment.Journal of Speech and Hearing Research, 39, 1171–1181. ASHAWireGoogle Scholar Finn, P., Howard, R., & Kubala, R. (2005). Unassisted recovery from stuttering: Self-perception of current speech behavior, attitudes, and feelings.Journal of Fluency Disorders, 30, 281–305. Google Scholar Finn, P., & Ingham, R. J. (1989). The selection of “fluent” samples in research on stuttering: Conceptual and methodological considerations.Journal of Speech and Hearing Research, 32 , 401–418. LinkGoogle Scholar Finn, P., & Ingham, R. J. (1994). Stutterers’ self-ratings of how natural speech sounds and feels.Journal of Speech and Hearing Research, 37, 326–340. LinkGoogle Scholar Ingham, R. J., & Cordes, A. K. (1997). Selfmeasurement and evaluating treatment efficacy.In R. F. Curlee&G. M. Siegel (Eds.), Nature and treatment of stuttering: New directions (pp. 413–437). San Diego, CA: Singular. Google Scholar Ingham, R. J., Warner, A., Byrd, A., & Cotton, J. (2006). Speech effort measurement and stuttering: Investigating the chorus reading effect.Journal of Speech, Language, and Hearing Research, 49, 660–670. LinkGoogle Scholar Kubala, R. (1998). Spontaneous recovery from stuttering: Analysis of current speaking status from interview data.Unpublished master’s thesis, University of New Mexico, Albuquerque, Nm. Google Scholar Martin, R. R., Haroldson, S. K., & Triden, K. A. (1984). Stuttering and speech naturalness.Journal of Speech and Hearing Disorders, 49, 53–58. LinkGoogle Scholar Perkins, W. H. (1971). Speech pathology: An applied behavioral science.St. Louis, MO: C. V. Mosley. Google Scholar Perkins, W. H. (1990). What is stuttering?.Journal of Speech and Hearing Disorders, 55, 370–382. LinkGoogle Scholar Smit, A. B., Hand, L., Freilinger, J. J., Bernthal, J. E., & Bird, A. (1990). The Iowa Articulation Norms Project and its Nebraska replication.Journal of Speech and Hearing Disorders, 55 , 779–798. LinkGoogle Scholar Starkweather, C. W. (1987). Fluency and stuttering.Englewood Cliffs, NJ: Prentice Hall. Google Scholar Wingate, M. E. (1984). Fluency, dis-flu-ency, dysfluency, and stuttering.Journal of Fluency Disorders, 17, 163–168. Google Scholar Wood, D. (2001). In search of fluency: What is it and how can we teach it?.The Canadian Modern Language Review, 57, 573–589. Google Scholar Additional Resources FiguresReferencesRelatedDetails Volume 17Issue 2August 2007Pages: 14-17 Get Permissions Add to your Mendeley library History Published in issue: Aug 1, 2007 Metrics Downloaded 51 times Topicsasha-topicsleader-topicsasha-sigsasha-article-typesCopyright & Permissions© 2007 American Speech-Language-Hearing AssociationPDF DownloadLoading ...
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".