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Record W2336826924 · doi:10.3109/02699206.2016.1157208

Phonological assessment and analysis tools for Tagalog: Preliminary development

2016· article· en· W2336826924 on OpenAlexafffundabout
Rachelle Kay Chen, Barbara May Bernhardt, Joseph Paul Stemberger

Bibliographic record

VenueClinical Linguistics & Phonetics · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsTagalogLinguisticsPsychologyNorm (philosophy)Computer scienceNatural language processingPolitical science

Abstract

fetched live from OpenAlex

Information and assessment tools concerning Tagalog phonological development are minimally available. The current study thus sets out to develop elicitation and analysis tools for Tagalog. A picture elicitation task was designed with a warm-up, screener and two extension lists, one with more complex and one with simpler words. A nonlinear phonological analysis form was adapted from English (Bernhardt & Stemberger, 2000) to capture key characteristics of Tagalog. The tools were piloted on a primarily Tagalog-speaking 4-year-old boy living in a Canadian-English-speaking environment. The data provided initial guidance for revision of the elicitation tool (available at phonodevelopment.sites.olt.ubc.ca). The analysis provides preliminary observations about possible expectations for primarily Tagalog-speaking 4-year-olds in English-speaking environments: Lack of mastery for tap/trill 'r', and minor mismatches for vowels, /l/, /h/ and word stress. Further research is required in order to develop the tool into a norm-referenced instrument for Tagalog in both monolingual and multilingual environments.

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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
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 head0.108
GPT teacher head0.443
Teacher spread0.335 · 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 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

Citations11
Published2016
Admission routes3
Has abstractyes

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