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Record W2614223588 · doi:10.1044/persp2.sig17.21

Crosslinguistic Phonological Development: An International Collaboration

2017· article· en· W2614223588 on OpenAlexaffabout
Barbara May Bernhardt, Joseph Paul Stemberger, Daniel Bérubé

Bibliographic record

VenuePerspectives of the ASHA Special Interest Groups · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of OttawaUniversity of British Columbia
Fundersnot available
KeywordsLinguisticsTagalogBulgarianPortugueseGermanSemitic languagesMandarin ChineseRomance languagesIcelandicPhonological awarenessTurkishHistoryPhonologyEuropean PortugueseIndo-European languagesArabicReading (process)

Abstract

fetched live from OpenAlex

An international study is investigating phonological development in 12 languages: Romance (Canadian French, Granada, Mexican and Chilean Spanish, and European Portuguese); Germanic (German, English, Swedish, and Icelandic); Semitic (Kuwaiti Arabic); Asian (Japanese, Mandarin); South Slavic (Bulgarian, Slovene). Additional phonological assessment materials have been created for Anishinaabemowin (Algonquian, Canada), Brazilian Portuguese, European French, Punjabi, Tagalog, and Greek. The study has two purposes: (a) to investigate crosslinguistic patterns in phonological development; and (b) to develop assessment tools and treatment activities. Equivalent crosslinguistic methodologies include: (a) single word lists for elicitation that reflect major characteristics of each language; (b) data collection and transcription by native speakers; (c) participant samples of 20–30 preschoolers (ages 3 to 6) with typical versus protracted phonological development; and (d) data analysis supported by Phon, a phonological analysis program. The current paper provides an overview of the study and introduces a website that offers free tutorials and materials for speech-language pathologists (SLPs).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.365
Teacher spread0.320 · 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.

Study designObservational
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

Citations6
Published2017
Admission routes2
Has abstractyes

Explore more

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