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Record W2945259153 · doi:10.1215/00031283-7587892

Bag Across the Border

2019· article· en· W2945259153 on OpenAlexaboutno aff
Julia Thomas Swan

Bibliographic record

VenueAmerican Speech · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyGentrificationDemographicsSociocultural evolutionDistribution (mathematics)GeographyInterpersonal tiesDemographic economicsSociologyEconomic geographyPolitical scienceEconomic growthDemographySocial scienceLawAnthropologyEconomics

Abstract

fetched live from OpenAlex

Prior research documents /æ/ raising and tensing when followed by /g/ in words like bag in the Pacific Northwest, particularly in Seattle. The present study compares /æg/ raising among speakers from Seattle, Washington, and Vancouver, British Columbia, and explores the social motivations for its use. The findings show that while the feature occurs in both cities, its social distribution is not identical. Different age and gender distributions and varying metalinguistic commentary raise questions about the trajectory of change in each city. Nonetheless, speakers’ realizations of raised bag are associated with similar sociocultural backgrounds and ideologies. In Seattle, bag raisers have multigenerational ties to the area, take strong ideological stances against changes in the area’s industries and economy, and oppose “gentrification.” Nonraisers have more international ties, show stronger interest in moving elsewhere, and embrace Seattle’s new industries. In Vancouver, BAG raisers describe growing up as Caucasian Canadians in majority Asian neighborhoods and emphasize the changing demographics and increased cost of living. In both cities, bag raisers are ideologically opposed to perceived encroachment and take conservative stances toward changes in their city. This highlights that the West and Canada participate in some of the same sound changes and show similar, locally contextualized motivations for their use.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.367
Teacher spread0.355 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations31
Published2019
Admission routes1
Has abstractyes

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