Bibliographic record
Abstract
The possibility of assigning contractual rights to third parties has often been taken to suggest that they amount to a form of “property” or “asset.” This point has been seized upon by proponents of transfer-based accounts of contract law, which understand contract as a means of transferring existing rights instead of creating new rights and duties between its parties. In this article, I set out to critically examine the extent to which this assumed compatibility between transfer theories of contract and the assignment of contractual rights can truly be sustained. As I argue, only one version of transfer theory is able to properly account for the way in which assignment actually operates within the common law tradition, corresponding to the version that most closely resembles more orthodox promise theories of contract law by understanding contract as a transfer of rights directly against the person of the promisor. By contrast, I suggest that the dominant version of transfer theory, according to which contract amounts to a transfer of rights over external things, is unable to draw a full distinction between contract and a completed assignment of contractual rights and so is unable to explain the rules that govern the latter class of transaction at common law and in equity.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".