Bibliographic record
Abstract
In the mid 1990s, when I first became interested in the challenges facing hos pitality workers and their unions, there was relatively little literature outside of human resource management perspectives on the subject. It was as if research ers were misinterpreting the 'please do not disturb' signs guests hang on hotel room doorknobs as a message to leave the entire hospitality sector alone. Rare exceptions were Dorothy Sue Cobble's Dishing it Out and Roy Wood's study of working in catering and accommodation in the uk.1 In recent years, however, hospitality work has attracted a greater number of scholars interested in 'new' economy labour issues. Hotels are of particular interests as post-industrial workplaces that employ growing numbers of marginalized workers, including new immigrants, racialized workers, women, and young people in increasingly polarized global cities. New research addresses an imbalance in labour studies which has arguably tended to focus on workers in 'core' manufacturing indus tries at the frontlines of global economic restructuring and transition.2 Three
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".