Sharing Data in the Platform Economy: A Public Interest Argument for Access to Platform Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Airbnb is a ‘sharing economy’ platform that facilitates the booking of short-term accommodation. The company is premised on the idea that many urban dwellers have excess space – rooms in homes or apartments – or have space they do not use at certain periods of the year (entire homes or apartments while on vacation, for example) – and that a digital marketplace can maximise efficient use of this space by matching those seeking temporary accommodation with those having excess space. This characterization of Airbnb is open to challenge. Indeed, a number of studies, including ones by the Canadian Centre for Policy Alternatives, the City of Vancouver, and the NY State Attorney General suggest that a significant number of units for rent on Airbnb are offered as part of commercial enterprises. The description also belies Airbnb’s disruptive impact. The process of re-characterization and commodification of ‘surplus’ private spaces neatly evades the regulatory frameworks designed for the marketing of short-term accommodation and leaves licensed short-term accommodation providers complaining that their highly regulated businesses are being undermined by competition from those not bearing the same regulatory burdens. At the same time, the data that would otherwise be captured through regulatory processes is effectively privatized in the hands of Airbnb, which retains exclusive control over it. This poses a challenge to local and regional governments who regulate and tax short-term accommodation in the public interest. This paper explores the impact on cities of platform companies such as Airbnb from the perspective of data. It argues that platform-based short-term rental activities have a fundamental impact on what data are available to municipal governments who struggle to regulate in the public interest. The impacts of platform companies are therefore not just disruptive of incumbent industries; they are disruptive of planning and regulatory systems by masking activities and creating data deficits. Cities need to find solutions to this data deficit. Currently available solutions range from self-help type recourses such as data scraping, or entering into data-sharing agreements with the platform companies. Each of these has its challenges and drawbacks. Further action may be required by governments to ensure their data needs are adequately met, and the paper addresses some of these options.
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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 | 0.006 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 it