Familiarity and Conviction in the Criminal Justice System
Bibliographic record
Abstract
Abstract Eyewitnesses are likely to have some degree of familiarity with a perpetrator when a crime is committed. Despite the fact that the majority of crimes are committed by someone with whom the victim/witness is familiar, the majority of eyewitness research has focused on the identification of stranger perpetrators. It is critical to examine how familiarity may influence eyewitness accuracy. Familiarity can vary from a complete stranger to a very familiar other. This book explores the “middle ground” as it relates to the criminal justice system, namely describing perpetrators, eyewitness identification, and jury decision-making. The purpose of this book is to consolidate the literature that exists regarding familiarity and to apply this research to an eyewitness context. This book attempts to better understand how familiarity may impact eyewitnesses and to highlight key considerations when an eyewitness is familiar with a perpetrator while collecting eyewitness evidence and using it in a courtroom. This is achieved through an in-depth discussion of the definition of familiarity, the examination of critical social psychological and cognitive theory in relation to familiarity, a description of the current literature examining eyewitness familiarity, a discussion of familiarity evidence in the courtroom, and a proposal for future directions and research.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".