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Record W176659036 · doi:10.1017/s0008413100000153

Ethnic divergence in Montreal English

2014· article· en· W176659036 on OpenAlexaffabout
Charles Boberg

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsMcGill University
Fundersnot available
KeywordsEthnic groupDivergence (linguistics)IrishVariation (astronomy)JudaismImmigrationDominance (genetics)LinguisticsPhoneticsSociolinguisticsSociologyAustralian EnglishGermanGender studiesHistoryAnthropology

Abstract

fetched live from OpenAlex

Abstract This article reports on a study of ethnic variation in the phonetics of Montreal English. The speech of 93 native speakers of Montreal English from three ethnic groups, British-Irish, Italian and Jewish, was recorded and subjected to acoustic analysis. Several statistically significant differences among the ethnic groups were identified. The present paper undertakes an apparent-time analysis of these differences, to see whether they are getting smaller over time, as might be expected under the assumption that post-immigrant generations gradually assimilate to the linguistic and cultural patterns of their adopted homelands. While Jewish Montrealers show some signs of convergence with the British-origin standard, Italians — especially young Italian men—appear to be diverging from that model. It is suggested that the unusual persistence and even intensification of ethno-phonetic variation in English-speaking Montreal reflects both the residential and social self-segregation of its ethnic communities and the local dominance of French.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.544
Threshold uncertainty score0.906

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.276
Teacher spread0.257 · 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

Citations42
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of Linguistics / La revue canadienne de linguistiqueSame topicLinguistic Variation and MorphologyFrench-language works237,207