Bibliographic record
Abstract
In this article, I propose a model for understanding the concept of ownership that I call the ‘exclusivity model.’ Like many of the contemporary critics of the ‘bundle of rights’ approach to ownership, I insist that ownership is a legal concept with a well-defined structure. I differ from most of them, however, in the model of ownership that I believe to be at work in property law. Most of these critics propose a model of ownership that emphasizes the owner's right to exclude non-owners from the owned thing as the central defining feature of ownership. I call this the ‘boundary approach’ to highlight its fixation on the owner's power to decide who may cross the boundaries of the owned thing. But this, I argue, makes it impossible for the boundary approach to explain adequately the many subsidiary rights in things that coexist with the rights of owners. Indeed, I argue that when we look more closely at the structure of ownership in property law, its central concern is not the exclusion of all non-owners from the owned thing but, rather, the preservation of the owner's position as the exclusive agenda setter for the owned thing. So long as others – whether they be holders of subsidiary property rights or strangers to the property – act in a way that is consistent with the owner's agenda, they pose no threat to the owner's exclusive position as agenda setter.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 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".