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Record W2886490657 · doi:10.5334/labphon.104

North American /l/ both darkens and lightens depending on morphological constituency and segmental context

2018· article· en· W2886490657 on OpenAlexaff
Sara Mackenzie, Erin A. Olson, Meghan Clayards, Michael Wagner

Bibliographic record

VenueLaboratory Phonology Journal of the Association for Laboratory Phonology · 2018
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsMcGill UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsMorphemeContext (archaeology)Variation (astronomy)PsychologyLinguisticsCategorical variableWord (group theory)Class (philosophy)Realization (probability)HistoryMathematicsPhilosophyComputer scienceArtificial intelligencePhysicsStatistics

Abstract

fetched live from OpenAlex

<p class="p1">It is uncontroversial that, in many varieties of English, the realization of /l/ varies depending on whether /l/ occurs word-initially or word-finally. The nature of this effect, however, remains controversial. Previous analyses alternately analyzed the variation as darkening or lightening, and alternately found evidence that the variation involves a categorical distinction between allophones or a gradient scale conditioned by phonetic factors. We argue that these diverging conclusions are a result of the numerous factors influencing /l/ darkness and differences between studies in terms of which factors are considered. By controlling for a range of factors, our study demonstrates a pattern of variability that has not been shown in previous work. We find evidence of morpheme-final darkening and morpheme-initial lightening when compared to a baseline of morpheme-internal /l/. We also find segmental effects such that, in segmental contexts which independently darken /l/, one can observe /l/ lightening, and contexts which independently lighten /l/ can make lightening effects undetectable. Morphological and prosodic effects are hence sometimes trumped by segmental context. Once contextual effects are controlled for, there is evidence both for morphologically-conditioned /l/-darkening and for morphologically-conditioned /l/-lightening, both of which can be understood as a result of prosodic differences reflecting morphological junctures.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.297
Teacher spread0.281 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

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