Bibliographic record
Abstract
In this paper I try to argue that the special sort of structure that links legal rules to their exceptions, namely, the structure of defeasibility, provides an account of rule-based decision making which is not exactly equivalent to rule-bound decision making, that is, to invariably following the rules. While defeasibility provides conceptual space for the categorical guidance of rules, it also allows for their non-absolute character, or their revision in the face of countervailing factors. I have also argued that something like a defeasible conception of rationality is what is required for the theory of games. What is needed there is a conception of rationality that, like rules, is sufficiently strong to provide some prima facie guidance for conduct at various points of choice, while at the same time not so strong as to preclude (empirically or logically) the possibility of being at those points of choice at all. Both legal rules and the rules of rationality are neither wholly descriptive nor wholly prescriptive in nature. In this they are quite different from both scientific laws, which are taken to provide descriptions of what `is`, and moral laws, which are thought to provide prescriptions for what `ought` to be. Both legal rules and the rules of rationality seem to bridge this is-ought divide. It should not be surprising, therefore, that legal rules and the rules of rationality might have a common structure, and that this structure, a structure that needs to accommodate both regularity and (an independent) particularity, might be provided by the concept of defeasibility.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".