The CSI Effect and the Canadian and the Australian Jury*<sup>,†</sup>
Bibliographic record
Abstract
Television shows, such as CBS's CSI and its spin-offs CSI: Miami; CSI: Las Vegas; and CSI: New York, have sparked the imagination of thousands of viewers who want to become forensic scientists. The shows' fictional portrayals of crime scene investigations have prompted fears that jurors will demand DNA and other forensic evidence before they will convict, and have unrealistic expectations of that evidence. This has been dubbed the "CSI effect." This phenomenon was explored using results from a Canadian study based on 605 surveys of Canadian college students who would be considered jury-eligible and Australian quantitative and qualitative findings from a study that surveyed and interviewed real posttrial jurors. Information about the way jurors deal with forensic evidence in the context of other evidence and feedback about the way in which understanding such evidence could be increased were gained from both these studies. The comparison provides insights into the knowledge base of jurors, permitting adaptation of methods of presenting forensic information by lawyers and experts in court, based on evidence rather than folklore. While the Canadian juror data showed statistically significant findings that jurors are clearly influenced in their treatment of some forensic evidence by their television-viewing habits, reassuringly, no support was found in either study for the operation of a detrimental CSI effect as defined above. In the Australian study, in fact, support was found for the proposition that jurors assess forensic evidence in a balanced and thoughtful manner.
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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 | 0.015 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".