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Record W4221069080 · doi:10.1017/s0022226722000032

Shift in Harmonic Serialism

2022· article· en· W4221069080 on OpenAlexaff
FREDERICK GIETZ, Peter Jurgec, MAIDA PERCIVAL

Bibliographic record

VenueJournal of Linguistics · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOptimality theoryRule-based machine translationComputer scienceComplement (music)HarmonicSpeech recognitionFeature (linguistics)TypologyLinguisticsNatural language processingAlgorithmMathematicsArtificial intelligenceAcousticsPhilosophyHistoryPhonologyPhysics

Abstract

fetched live from OpenAlex

Harmonic Serialism is a serial version of Optimality Theory in which Gen is restricted to one operation at a time. What constitutes one operation has been a key question in the literature. This paper asks whether shift, in which a feature moves/flops from one segment to another, should be considered an operation. We review three pieces of evidence that suggest so. We show that only the one-step shift analysis can capture the tonal patterns in Kibondei and the segmental patterns in Halkomelem; grammars that rely on spreading or floating features cannot. We complement these findings with a factorial typology in which the one-step shifting grammars predict several attested patterns that the grammars without one-step shift cannot. We conclude that shift must be a single operation in Harmonic Serialism.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.379
Teacher spread0.326 · 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 designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

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