Embodied intersectionality at work in hotel housekeeping in Sweden
Bibliographic record
Abstract
This paper examines the experiences of hotel housekeepers in Sweden, focusing on how their embodied identities, shaped by factors like gender, race, and migration background, influence and are influenced by their work. Placing the body at the centre of our research, we explore how embodied intersectionality is situated and contextually produced as lived experience in hotel workplaces. The way in which intersectional power dimensions are played out and performed is closely interconnected to bodies and the embodiment of roles, duties, identities and expectations inherent in the occupational context of the workplace. Through working participant observation, we explore the daily routines and bodily demands of housekeeping work, a low-status, feminised occupation predominantly filled by women and migrants. Our paper emphasises how embodied intersectionality operates within the workplace, revealing power dynamics, challenges, and subtle forms of resistance enacted by the workers. Our analysis illustrates that the ways in which hotel housekeeping work is carried out is shaped by expectations on what it means to be a housekeeper as much as it is by wider intersectional factors and embodied identities. We conclude by advocating for a greater emphasis on the working body in workplace studies to understand the nuanced realities of such marginalised labourers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".