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Record W3096802457 · doi:10.1075/bpa.10.12ros

Using Phon to analyze phonological and speech data

2020· book-chapter· en· W3096802457 on OpenAlex
Yvan Rose

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBilingual processing and acquisition · 2020
Typebook-chapter
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceSet (abstract data type)Range (aeronautics)SoftwareEngineering

Abstract

fetched live from OpenAlex

Abstract Phon, the software program behind the PhonBank database, offers a set of functions useful to the analysis of phonological and acoustic data. In this paper, I provide an illustrated description of the most central query and reporting functions currently available within Phon, including a discussion of how these functions are readily applicable to research on second language acquisition. I also describe, however with a lesser amount of detail, how the Phon query functions can be used to obtain acoustic measurements of audio recordings. Finally, I provide an overview of pre-defined analyses built into Phon which are commonly used in research on phonological development and speech disorders. Throughout the paper, I discuss related topics, which range from technical issues in data preparation to the larger, community-level importance of data sharing as a condition for the future of this software and database development projects.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.170
GPT teacher head0.410
Teacher spread0.240 · 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