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Record W2504659467 · doi:10.1075/la.194.08rei

Towards a bottom-up approach to phonological typology

2012· book-chapter· en· W2504659467 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.

Bibliographic record

VenueLinguistik aktuell · 2012
Typebook-chapter
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsSimple (philosophy)Computer scienceRepresentation (politics)Set (abstract data type)Point (geometry)Feature (linguistics)Theoretical computer scienceTypologyRange (aeronautics)ComputationPhonologyArtificial intelligenceLinguisticsMathematicsAlgorithmProgramming languageEpistemologyGeographyEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The set of combinatoric possibilities of even simple formal systems explodes quickly. Adopting (perhaps overly) simple assumptions about phonological representation and computation, we show that, with just a handful of featural primitives, the number of possible segments, the number of possible inventories and the number of possible rule targets quickly reaches shockingly high levels. Not only is this result inevitable for pretty much any feature system, but it is also desirable. The crucial point is to define sets (of segments, inventories, or rule targets) intensionally, and see that we can account for a vast range of phenomena using a minimal toolkit, in parallel to recent evo-devo work in biology. Understanding the combinatorics is a step towards a biolinguistic approach to phonology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.745
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.026

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 head0.086
GPT teacher head0.350
Teacher spread0.264 · 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