Prediction, persuasion, and the jurisprudence of behaviourism
Bibliographic record
Abstract
There is a growing literature critiquing the unreflective application of big data, predictive analytics, artificial intelligence, and machine-learning techniques to social problems. Such methods may reflect biases rather than reasoned decision making. They also may leave those affected by automated sorting and categorizing unable to understand the basis of the decisions affecting them. Despite these problems, machine-learning experts are feeding judicial opinions to algorithms to predict how future cases will be decided. We call the use of such predictive analytics in judicial contexts a jurisprudence of behaviourism as it rests on a fundamentally Skinnerian model of cognition as a black-boxed transformation of inputs into outputs. In this model, persuasion is passé; what matters is prediction. After describing and critiquing a recent study that has advanced this jurisprudence of behaviourism, we question the value of such research. Widespread deployment of prediction models not based on the meaning of important precedents and facts may endanger the core rule-of-law values.
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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.054 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".