Choice and Background Knowledge: How do Individuals Evaluate Accumulating Evidence in A Murder Scenario?
Bibliographic record
Abstract
Can the simple act of selecting a possible suspect of a crime bias the evaluation of the evidence? Does the typicality of the crime impact the assessment of guilt of a suspect? In two experiments, we examine these two questions and find some remarkable results with implications for law enforcement and jury deliberation. Experiment 1 data show that by allowing participants to choose a most-likely-perpetrator, guilt ratings were substantially higher compared to participants who were not allowed to make a choice. This difference persisted after reading a further body of incriminating evidence. In experiment 2 participants were provided with general and specific background information relevant to a suspect, in other words how common was the crime-suspect scenario. When provided with high plausibility compared to low plausibility information, participants gave higher guilt ratings that persisted after further evidence. The results are interpreted in terms of argument theory which provides a parsimonious explanation of the data. These results have implications for the conduct of investigations, for example: putting in place procedures that minimize the effects of suspect prioritization and background information.
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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 | 0.008 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".