Place-Bound Jobs at the Intersection of Policy and Management: Comparing Employer Practices in U.S. and Canadian Chain Restaurants
Bibliographic record
Abstract
Debate about the United States’ minimum wage spiked several years ago at a time when its role in influencing employment conditions had become complicated by firms’ increasing use of job outsourcing and “offshoring.” Yet the latter labor strategies are not obviously applicable to employment revolving around in-person transactions between workers and customers, or “place-bound” work. Such jobs present an opportunity for studying human resource management, and the capacity of public policy to shape it, when policy may be at its most influential over employer practices. The current article considers such a case, investigating how minimum wage rates, other public policies and programs associated with work, and firms’ human resource practices interact in the place-bound position of restaurant waiter. Using new data collected from managers of a sample of 21 sites of two low-end, full-service restaurant chains, the author examined the relationships between management practices for wages and tips, fringe benefits, and staffing and scheduling and the public policy contexts in which they were embedded in suburban Seattle, Chicago, and Vancouver, British Columbia. The author found that employer practices varied by geographic area as a product of contrasts in public regulation of employers as well as supports to workers and families; that employer practices varied between the two chains, independent of geographic location; and that those practices were often poised to have dramatic impacts on waiters’ income and benefits access. The author concludes by discussing some of the limitations of and prospects for applying public tools to promote the quality of private, hourly jobs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".