Bibliographic record
Abstract
Over the course of the Covid-19 pandemic, restauranteurs and wage-earning restaurant staff found themselves positioned as the focus of an immense amount of mainstream media attention. Never has the restaurant industry been the recipient of such frequent and consistent mainstream coverage, yet scholars have yet to critically engage with the available media discourse. This research explores the mainstream media depiction of the restaurant industry and challenges journalistic practices which prioritize the voices and ideological perspectives of those atop the restaurant industry hierarchy. To demonstrate this phenomenon, I engaged in a critical discourse analysis of 55 published online news articles through the theoretical lens proposed by Gayatri Spivak. The sample was examined to demonstrate who was afforded the discursive space to utilize their voice and share their ideological disposition as well as the ways in which the discursive voice found within the sample shaped a representation of wage-earning restaurant staff. The primary findings of this paper reveal that wage-earning restaurant staff, within the selected sample, were discursively silenced and not provided with an adequate opportunity to share their experience of working in a customer-facing position throughout the Covid-19 pandemic. Wage-earning restaurant staff were rarely afforded the opportunity to speak, however, they were spoken for. I argue throughout this paper that the voice of wage-earning restaurant staff is discursively crafted by those atop the restaurant industry hierarchy and that this phenomenon serves to validate traditional restaurant industry hierarchical structures and reinforce hegemonic ideological perspectives. This study emphasizes the need for journalists to embrace the theoretical disposition of a standpoint theorist and strive to ensure that members of subordinated populations are not subject to the imposition of an inauthentic voice.
 
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".