Bibliographic record
Abstract
A common accusation against law and economics is that it is based on unrealistic and unreasonable assumptions, such as claiming that people behave rationally. This accusation may very well be true. But it should not stand in the way of progress in legal analysis. The reason is that when something is assumed about facts — for example, how people behave or, alternatively, about the best way to interpret a set of judgments — the test of this assumption is in whether the hypotheses built on it are supported or refuted by other facts. If an assumption does not lead to accurate predictions, it can easily be discarded. In contrast, conceptual analysis of law that tries to assess the nature of a legal norm or field, for example establishing whether investment treaty arbitration is a part of public international law or not, is not assuming anything about facts. Because the only substance that is played with is concepts, no facts can be brought to refute the argument, only competing narratives. The purpose of this paper is to explain why the process of making assumptions is necessary for legal scholarship and why it is impossible to understand the law without assumptions and it could be dangerous to try to do so.
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 | 0.059 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.070 |
| Scholarly communication | 0.017 | 0.058 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".