Bibliographic record
Abstract
This book shines a spotlight on the causes and consequences of working poverty, revealing how the lives of low-wage workers are affected by differences in health care, labor, and social welfare policy in the United States and Canada. Dan Zuberi's conclusions are based on survey data, eighteen months of participant observation fieldwork, and in-depth interviews with seventy-seven hotel employees working in parallel jobs on both sides of the border. Two hotel chains, each with one union and one non-union hotel in Seattle and Vancouver, provide a vivid crossnational comparison because they are similar in so many regards, the one major exception being government policy.Zuberi demonstrates how labor, health, social welfare, and public investment policy affect these hotel workers and their families. His book challenges the myth that globalization necessarily means hospitality jobs must be insecure and pay poverty wages and makes clear the critical role played by government policy in the reduction of poverty and creation of economic equality. Zuberi shows exactly where and how the social policies that distinguish the Canadian welfare state from the U.S. version make a difference in protecting Canadian workers from the hardships that burden low-wage workers in the United States. Differences That Matter , which is filled with first-person accounts, ends with policy recommendations and a call for grassroots community organizing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".