Safety in Numbers: A Policy‐Capturing Study of the Alibi Assessment Process
Bibliographic record
Abstract
Summary A policy‐capturing analysis of alibi assessments was conducted. University students (N = 65), law enforcement students (N = 21), and police officers (N = 11) were provided with 32 statements from individuals supporting a suspect's alibi (i.e., alibi corroborators) and asked to assess the believability of the alibi, the suspect's guilt, and whether they would arrest the suspect. Each statement was composed of five binary features (i.e., relationship between corroborator and accused, age of corroborator, amount of available corroborators, alibi corroborator's confidence in their account, and memorability of the target event). Results showed that there was much parity in the type of information used to assess alibis across the samples. Specifically, we found that 90% of participants' decision policies included the amount of corroborators. Participants also relied upon, albeit to a lesser extent, the suspect–corroborator relationship and the age of the corroborator when assessing the alibi. The potential implications of these findings for understanding how people assess alibi corroborators are discussed. Copyright © 2016 John Wiley & Sons, Ltd.
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 | 0.023 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".