The Division of Cognitive Labour in Law
Bibliographic record
Abstract
Law makes assumptions about the workings of the human mind, assumptions that are common in folk psychology, i.e. our unreflective way of understanding people's cognition and behaviour, but which are contradicted by scientific study of psychology. This chapter tries to flesh out part of the myths created by our folk psychological understanding of institutions and those who operate in them: the fictional cognitive abilities of the agents postulated by law. It focuses in particular on how the law often seems to overestimate the human ability to solve problems individually and underestimate the importance of the division of cognitive labour. I will call this set of assumptions cognitive perfectionism. I will distinguish between two aspects of cognitive perfectionism: the assumption that agents are endowed with perfect rationality, that they behave like a homo oeconomicus (an assumption often criticised in the literature), which I will call rationality perfectionism, and the assumption that agents are endowed with the ability to process extremely large amounts of information (an aspect less considered in the literature so far), which I will call knowledge perfectionism. These assumptions give a distorted picture of the goals law can reasonably aspire to and of the best possible means of achieving them. At the same time, they seem to function as regulative ideals somehow essential to, and maybe even inseparable from and desirable in, our legal practice.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.082 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".