Platforms as Private Governance Systems – The Example of Airbnb
Bibliographic record
Abstract
Online platforms create legal systems that can best be described as private governance systems. By private governance we refer to the fact that a private actor can take on the roles as regulator, implementer and dispute resolution body, thereby mirroring the classical roles of the state and potentially replacing state governance with an alternative, private legal order. As an example, the Airbnb platform (www.airbnb.com) regulates the rights and duties between users of the platform (hosts and guests), it implements these rights and duties by facilitating supervision mechanisms such as ratings and reviews, and it provides dispute resolution mechanisms for the users. The increasing societal role and impact of online platforms makes it pertinent to consider to what extent these private governance systems can safeguard the public values and interests which state legal orders seek to promote and protect. In this article, we use the concept of private governance to make a case study of the private legal order of Airbnb. Our analysis shows that the private governance system created by Airbnb is concerned not only with commercial matters, but also with public values as known in state legal orders. However, it also shows that the private governance system created by Airbnb can have an undermining effect on state legal orders.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".