Bibliographic record
Abstract
I begin by commenting on the failure of current strategies to help us understand the “actual” decision-making process in law. Then I use the work of Bernard Lonergan (1904-1984) and Philip McShane, two Canadian philosophers, to present a plausible method of introspection. Will we ever be able to investigate the process of discovery in law? How can we investigate the “actual” legal decision-making process? Many legal theorists who study legal reasoning accept that there is a clear distinction between the process whereby legal decisions are actually reached and the process whereby they are publicly justified. Discovery is one thing, but the legal justification of an outcome is another matter. These theorists even claim that legal theorists need not bother examining the discovery process; their proper subject matter is legal justification. Joxe Bengoetexea (1993, 118-119) even doubts whether the discovery process can be studied. Contemporary legal theorists, while claiming that it would be worthwhile to study the “actual” decision-making process, state that it is an activity that is very difficult to study. In a his book called The Judicial Application of Law, Jerzy Wroblewski states that the psychology material is less accessible than written case reports. It is difficult to obtain data because discussions among judges about cases are confidential. Further, he believes that the method of investigation is restricted to introspection, meaning the self-analysis of the decision-maker. In fact it is generally accepted that investigations of the process of discovery, the process whereby legal decisions are “actually” reached, should be left to psychologists. However, the results of cognitive psychologists have not been encouraging. The psychologists themselves admit their work is more descriptive than it is explanatory. 1 Wroblewski captures the current situation in the legal
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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 | 0.152 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.129 |
| Scholarly communication | 0.020 | 0.052 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.021 | 0.046 |
| Insufficient payload (model declined to judge) | 0.004 | 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".