Spatio-temporal dynamics of platform labour: short-term rental cleaning labour intermediaries and student-migrant-workers
Bibliographic record
Abstract
Although short-term rental platforms (e.g. Airbnb) are often not considered labour platforms, their suppliers must contend with demands on labour structured by the platforms. Using Lefebvre’s rhythmanalysis, this article examines the spatio-temporal dynamics of cleaning labour for STR platforms through the experiences of student-migrant-workers in Montreal between 2017 and 2020. I argue that yield-focused STR operators (i.e. owner-operators and managers) encounter spatio-temporal friction (or arrhythmia) in their efforts to outsource STR cleaning labour, but some labour intermediaries have responded with strategies to bring this conflict into harmony (eurhythmia) by incorporating the lives and labour of student-migrant-workers. At once limited in their employment options and not exclusively dependent on this work, student-migrant-workers illustrate characteristics that these intermediaries require of the workers they assemble and the flexible labour force upon which STR platforms depend. Rather than replacing labour intermediaries, online platforms have created new spatio-temporal dynamics for labour and new opportunities for those who profit from assembling the labour forces that address them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".