Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how claims to “ownership” are asserted over publicly accessible platform data and critically assess the nature and scope of rights to reuse these data. Design/methodology/approach Using Airbnb as a case study, this paper examines the data ecosystem that arises around publicly accessible platform data. It analyzes current statute and case law in order to understand the state of the law around the scraping of such data. Findings This paper demonstrates that there is considerable uncertainty about the practice of data scraping, and that there are risks in allowing the law to evolve in the context of battles between business competitors without a consideration of the broader public interest in data scraping. It argues for a data ecosystem approach that can keep the public dimension issues more squarely within the frame when data scraping is judicially considered. Practical implications The nature of some sharing economy platforms requires that a large subset of their data be publicly accessible. These data can be used to understand how platform companies operate, to assess their compliance with laws and regulations and to evaluate their social and economic impacts. They can also be used in different kinds of data analytics. Such data are therefore sought after by civil society organizations, researchers, entrepreneurs and regulators. This paper considers who has a right to control access to and use of these data, and addresses current uncertainties in how the law will apply to scraping activities, and builds an argument for a consideration of the public interest in data scraping. Originality/value The issue of ownership/control over publicly accessible information is of growing importance; this paper offers a framework for approaching these legal questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".