You wants aioli on that seal carpaccio, luh? \nOn the viability of local dialect in the St. John's restaurant industry
Bibliographic record
Abstract
Nearly all dialects experience variation and change, and Newfoundland English (NE) is no \nexception. As the growing service sector of Newfoundland and Labrador's capital city, St. John's, \nstrives to accommodate a competitive global enterprise culture (Harvey 2005), I question whether \nspoken language is being reflected in these values. The aim of this thesis is twofold. First, to centralize \nthe workplace in variationist sociolinguistics research, looking specifically at two phonetic variables \nwithin the St. John's restaurant industry. Second, to pursue an emic perspective (Eckert 2000) in \nvariationist research by inaugurating organizational identification (Cheney 1983; De Decker 2012), or \nsense of oneness with one's place of work, as a new framework for analyzing linguistic variation. \nI recorded a series of hour long, Labovian-style, semi-structured interviews with sixteen female \nrestaurant servers and hostesses from Newfoundland, working at any of six types of restaurant in St. \nJohn's classified according to traditional sociolinguistic categories. Participants also completed an \nOrganizational Identification Questionnaire (OIQ) (Gautam et al. 2004). Interviews elicited tokens \ncorresponding to the phonetic variables of interest, slit fricative (Clarke 1986) and creaky voice (Yuasa \n2010) in both casual and careful speech. Following interview transcriptions and coding of the two \nvariables, the influence of several external factors on OI and linguistic behaviour was analyzed with \nmultiple mixed-effects logistic regression models run using the glmer package in R (Johnson 2009). \nThe traditional variationist model shows local and expensive restaurant employees to exhibit \nsignificantly less creak and affrication than employees of non-local and inexpensive restaurants \n(p<0.000). The OI model presents nearly identical results, in that OI is strongest in local and expensive \nrestaurants and there is a strong negative correlation between OI and use of creaky voice and slit \nfricative. Indeed, the relative lack of the observed variables among higher end restaurant employees, \ncoupled with incrementally higher OI, points to the linguistic capital (Bourdieu 1991) of neutral \nspeech, which appears to play a role in shaping the socially salient organizational image of the restaurant industry as a whole. \nOverall, the OI model is seen as more meaningful than traditional variationist models because \nrather than indexing an employee's linguistic behaviour to a fixed restaurant category, OI is viewed as \nconsonant with a restaurant's linguistic identity at a particular time and place, with ethnographies \nproviding emic, descriptive categorizations of restaurants, further qualifying the interaction between OI \nand linguistic behaviour. It is hoped that this study encourages a discussion about how alignment with \nperceived market identity relates to linguistic capital, how the economic evaluation of specific dialects \nis temporarily manifested in variations of spoken English in the workplace, and how OI provides an \nemic perspective for analyzing workplace variation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".