Cleaning Up After Globalization: An Ergonomic Analysis of Work Activity of Hotel Cleaners
Bibliographic record
Abstract
Hotels and hotel chains are responding to globalization and increased competition through new marketing initiatives, employment practices, and restructuring decisions that are intensifying the work of cleaners. In this paper, we report on how such work intensification at two hotels in Montréal, Canada, is changing the nature of cleaners’ jobs. Specifically, we found that the numbers of operations to be completed, the numbers and weights of items to be cleaned, and the effort involved have all increased. “Flexible” employment relationships and outsourcing have also worsened cleaners’ workloads. In response to our research, the labour union representing cleaners has negotiated a lower number of room assignments per cleaner, as well as an improved way of taking into account the variability of work when determining the quota of rooms to be cleaned. Despite this, new marketing strategies continue to intensify work. We conclude that standards and regulation on a governmental level are a necessary complement to union actions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".