Familiarity and Conviction in the Criminal Justice System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it