Bibliographic record
Abstract
In this article, I revisit the arguments in, and address some concerns about, an earlier article of mine, ‘The Office of Ownership.’ This article makes two main points. The first is about the ways in which a transfer of property from one person to another affects the obligations of third parties. I continue to defend the earlier article’s claim that, by thinking about the obligations owed to owners of property as being owed to ‘the owner,’ rather than to the particular named person who happens to be the owner, we can maintain both the idea that property rights are in rem and the idea that they partake of private law’s distinctive bilateral normativity. The second is about the extent to which the notion of an office is helpful in thinking about ownership. There is reason to doubt that it is, notably because owners’ powers are typically not bounded in the way that office-holders’ powers seem to be. But I argue that, at least some of the time, particularly in contexts where owners are able to exercise powers that bind their successors in title, such as by creating easements, leases, or running covenants, the powers of ownership do seem bounded by their purpose.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".