Beyond Frequency: Perceived Realism and the <i>CSI</i> Effect
Bibliographic record
Abstract
Although police, lawyers, judges, and even some community members believe that CSI-type shows have seriously affected the criminal justice system (termed the CSI effect), empirical research has not demonstrated a link between crime television viewing and verdicts. However, the literature has established that higher frequencies of crime television viewing are associated with increased expectations of evidence, different attitudes toward evidence types, and varying self-reported levels of understanding of scientific evidence. The present study sought to extend our understanding of the influence of crime television on attitudes, expectations, and verdicts by examining the influence of perceived realism (i.e., the degree to which television programs are viewed as accurate and realistic depictions of the field that they portray) in this context, since some research has identified perceived realism as a moderator of the effects of television on attitudes. Participants were asked to play the role of mock jurors and read a trial transcript in which the Crown presented DNA evidence. Participants also indicated the frequency with which they watched crime television programs (frequency), as well as the degree to which they felt these programs were accurate and realistic depictions of the criminal justice system (perceived realism). Results revealed a number of interesting direct and indirect effects of both frequency of viewing and perceived realism on mock juror information processing, attitudes, and decision making, suggesting that in order to truly understand the effect that crime television may have on potential jurors, their frequency of watching must be considered in combination with the degree to which they perceive these programs as realistic depictions of the justice system.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".