Bibliographic record
Abstract
Introduction The similarity between a promise and a contract is so obvious that it is natural to suppose that there is much to be learned about one of these notions by studying the other, or even that the legal notion of a contract can be understood by seeing it as based on the moral idea of a promise. This article will examine some of the similarities between these two notions. These similarities are due to the fact that contract and promise arise in response to, and are consequently shaped by, some of the same underlying values. They are in this respect parallel ideas. But they respond to these values in different ways and are independent notions, neither of which is properly seen as based on the other. The law of contracts is clearly a social institution, backed by the coercive power of the state and subject to modification through judicial decisions and legislative enactments. Promising is also often seen as a social institution of a more informal kind, defined by certain rules which are not enacted but rather backed by moral argument and enforced through the informal sanction of moral disapproval. Many have argued that the wrong involved in breaking a promise depends essentially on the existence of a social practice of this kind. Hume, for example, maintains that fidelity to promises is “an artificial virtue,” dependent on the existence of a convention of keeping agreements, and other accounts of this kind have been advanced in our own day by Rawls and others.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".