Putting on the mask: Language work, ideology, and resistance in restaurant servers’ stories from the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic brought longstanding issues of power and precarity in restaurant service work to the fore (One Fair Wage 2020). However, insufficient attention has been paid to how these dynamics play out through language. To address this gap, this dissertation provides a computationally-assisted qualitative analysis of restaurant servers’ discourse about their own work experiences. I draw on two primary data sources: posts and comments on the Reddit forum /r/TalesFromYourServer from March 2020 to August 2021, and nine interviews with restaurant servers from August–December 2022; I subject these data to content, narrative, and discourse analysis. The analysis focuses on language commodification (Heller 2010; Bruzos 2023), emotional labour (Hochschild 1983; Grandey 2000), aesthetic labour (Warhurst, Nickson, et al. 2000), and their consequences for restaurant service workers, as well as how they cope with the challenges of this labour through online and in-person communities of coping (Korczynski 2003)—for example, by venting, by sharing scripts and behavioural strategies for dealing with difficult customers, and by collectively rejecting the management narrative that “the customer is always right”. Through analysis of servers’ narratives and metalinguistic commentary about their language practices at work, I illustrate that language commodification is not peculiar to “language work” (e.g., translation, speechwriting) but is found, along with the commodification of emotional and aesthetic practices, in (restaurant) service work. I argue, however, following Holborow (2018), that language commodification cannot be divorced from these other forms of commodified labour. Finally, I propose, with Bruzos (2023), a refocusing of language commodification research on the material impacts of such exploitation on workers and their acts of resistance to it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".