Bibliographic record
Abstract
The so-called sharing economy presents one of the most important and controversial regulatory dilemmas of our time — yet, surprisingly, it remains undertheorized. This Article supplies needed analysis. Specifically, the Article offers a regulatory model that distinguishes between two separate kinds of transactions: conventional economic transactions and those that rely on temporary access to goods and services that would otherwise go underutilized (what I call “access-to-excess” transactions). The regulatory regime that this Article proposes would distinguish between true access-to-excess transactions and conventional transactions. The model is rooted in a version of pluralist theory that posits that the state is responsible for cultivating a range of social institutions that offer meaningful economic and social alternatives to individuals.Recognizing access-to-excess transactions in a separate legal regime does not mean countenancing all access-to-excess activity in an under-regulated Wild West of markets. Pluralism has something to offer here as well: I argue that, properly understood, pluralistic principles do not endorse free-market and hands-off policies. Rather, they require state intervention to preserve existing choices, embed and balance diverse values (not only autonomy), ensure fair competition, and protect consumers and employees from strategic and opportunistic behaviors. Thus, pluralistic principles offer the normative foundation for inventive regulation — neither conventional nor free market — that can restrain some of the “sharing” economy’s harms without impeding innovation.Finally, the Article reverses the lens: The “sharing” economy serves as a real-life laboratory to reveal the operation of pluralistic theory and, thus, sheds light on the theory’s limitations. In particular, the “sharing” economy shows how the plasticity of pluralistic theory may enable harmful free-market policies to masquerade as “choice.”
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".