Organizing for Better Working Conditions and Wages: The Unite Here! Hotel Workers Rising Campaign
Bibliographic record
Abstract
This article examines some of the strategies and success of the UNITE HERE! union in its ongoing Hotel Workers Rising: Lifting One Another Above the Poverty Line campaign in the United States and Canada. This unique campaign has generated national attention in both the United States and Canada about issues facing hotel workers, including how changes in corporate policies aimed at pleasing the consumer - such as the shift to 'heavenly' beds - has had deleterious consequences for Room Attendants in terms of back injuries from lifting heavier mattresses. How successful has the UNITE HERE! been so far in terms of securing new contracts? What about in terms of organizing urban, suburban, and rural hotel employees? What barriers do unions face when organizing hotel workers? What does comparing union density rates in the hotel sector across cities reveal? After beginning to address some of these questions, this article concludes by providing some policy recommendations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".