Bibliographic record
Abstract
This article seeks to reclaim for property law and theory the centrality of two hitherto neglected questions: when does property matter and, to the extent that it does, precisely how. I argue that, in some cases, the property owner’s entitlement to exclude others has virtually nothing to do with the right to property; property, then, is epiphenomenal. At other times, an entitlement to exclude cannot exist independently of having a right to property. But even then – and this is where the second question concerning how property matters kicks in – there are important differences between excluding others for housekeeping purposes (say, ‘not now’) and denying access categorically (say, ‘not for you’). I therefore argue that the conventional identification of property with exclusion, or with exclusion and inclusion, obscures the difference that the right to property could, and should, make. Addressing the questions of when, and how, does property matter change the way we understand in theory and determine in practice what rights to exclude, and duties to include, do we have.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.077 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| 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".