Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".