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Record W2468025210 · doi:10.3765/amp.v2i0.3750

Long-Distance Phonotactics as Tier-Based Strictly 2-Local Languages

2016· article· en· W2468025210 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the Annual Meetings on Phonology · 2016
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLocalityPhonotacticsDependency (UML)Computer scienceSimple (philosophy)Class (philosophy)Blocking (statistics)Transparency (behavior)MathematicsLinguisticsArtificial intelligencePhonology

Abstract

fetched live from OpenAlex

This paper shows that the properties of locality observed for patterns of long-distance consonant agreement and disagreement belong to a well-defined and relatively simple class of subregular formal languages (stringsets) called the Tier-based Strictly 2-Local languages, and argues that analyzing them as such has desirable theoretical implications. Specifically, treating the two elements of a long-distance dependency as adjacent segments on the computationally defined notion of a tier allows for a unified account of locality that necessarily extends to the cross-linguistically variable behavior of neutral segments (transparency and blocking). This result is significant in light of the long-standing and persistent problems that long-distance dependencies have raised for phonological theory, with current approaches still predicting several pathological patterns that have little or no empirical support.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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