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Record W2803438997 · doi:10.1017/s0954394517000278

Dressing down up north: DRESS-lowering and /l/ allophony in a Scottish dialect

2018· article· en· W2803438997 on OpenAlexfundno aff
Sophie Holmes-Elliott, Jennifer Smith

Bibliographic record

VenueLanguage Variation and Change · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
FundersEconomic and Social Research CouncilUniversity of Toronto
KeywordsVariety (cybernetics)LinguisticsRange (aeronautics)HistoryGeographyEconomic geographyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract This study reports on a sociophonetic investigation of dress -lowering in a rural dialect in northeast Scotland. Previous analyses have indicated that this change is ongoing in a number of varieties worldwide, propelled by a combination of linguistic constraints and favorable associations with Anglo urban Californian varieties. In this paper we examine whether these influences play out in a relic dialect previously resistant to more supralocal changes. Through an analysis of a range of acoustic correlates, we track the progress of this change across three generations of speakers. Analysis of the constraints suggests that in this variety the change is driven by internal pressures, where it is significantly constrained by phonetic environment, specifically, following laterals. Further analysis of this environment reveals increasing distinction on the F2-F1 spectrum, where /l/s have become lighter in onsets and darker in codas. Our analyses reveal that these changes may be viewed as complementary, as they share the same acoustic correlates, suggesting that system-internal pressures are the primary driving force of dress -lowering in this variety.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.035
GPT teacher head0.316
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2018
Admission routes1
Has abstractyes

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