CHAPTER XII Making Sense of the Public Discourse on Airbnb and Labour: What about Labour Rights?
Bibliographic record
Abstract
IntroductionP opular sharing economy platforms, such as Airbnb, have been a frequent focus of public attention in recent years.Much of this attention has been driven by the numerous regulation challenges facing these platforms, either present ones through ongoing litigation, or prospective ones, through legislation drafting by governments.The media addresses evident legal issues, such as the fact that short-term rental laws are circumvented, as in the case of Quebec, 2 or the new regulations to curb illegal actions. 3These issues speak the loudest, as they are immediate or imminent.What is lost, or what might be lost, in terms of the labour rights of workers, doesn't appear as urgent an issue.Opinion pieces, among others, have touched on this issue, alerting the reader to unsuspected problems appearing through the cracks of the bright portrait painted by the platforms.It doesn't appear, however, that labour rights are a central part of the public discourse on the sharing economy.What can be gained (for hosts, drivers, consumers, and others), rather than what can be lost, seems to receive much more attention."Public discourse" is understood for the purposes of this chapter as being mainly constituted of media accounts on Airbnb, including news reports, opinion columns, and analytic journalism.It is understood as the information and analyses presented to the general population about Airbnb, rather than aimed at a specific audience such as academia.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".