Why Wasn’t O. J. Convicted? Emotional Coherence in Legal Inference
Bibliographic record
Abstract
In 1995, O. J. Simpson was tried for the murder of his ex-wife, Nicole Brown Simpson, and her friend, Ron Goldman, both of whom had been found with multiple knife wounds.To the surprise of many, the jury found Simpson not guilty of the crime, and many explanations have been given for the verdict, ranging from emotional bias on the part of the jury to incompetence on the part of the prosecution.Of course, there is also the possibility that, given the evidence presented to them, the jury rationally made the decision that Simpson was not guilty beyond a reasonable doubt.This paper evaluates four competing psychological explanations for why the jury reached the verdict they did:1. Explanatory coherence.The jury found O. J. Simpson not guilty because they did not find it plausible that he had committed the crime, where plausibility is determined by explanatory coherence.2. Probability theory.The jury found O. J. Simpson not guilty because they thought that it was not sufficiently probable that he had committed the crime, where probability is calculated by means of Bayes's theorem.3. Wishful thinking.The jury found O. J. Simpson not guilty because they were emotionally biased toward him and wanted to find him not guilty.4. Emotional coherence.The jury found O. J. Simpson not guilty because of an interaction between emotional bias and explanatory coherence.I will describe computational models that provide detailed simulations of juror reasoning for explanatory and emotional coherence, and argue that the latter account is the most plausible.Application to the Simpson case requires expansion of my previous theory of
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".