Bibliographic record
Abstract
This special issue of Court Review focuses on media matters. One provocative question related to media is the purported impact of an iconic television show, Crime Scene Investigation (CSI), on the judicial process. In the past few years, it has been frequently suggested, especially in the media, that judges, prosecutors, defense attorneys, and jurors have become influenced by CSI. The allegation is that the “CSI-effect” has resulted in an expectation that forensic evidence is required for successful criminal prosecutions. But is there (apologies to Gertrude Stein) a there there? Three articles in the special issue examine the so-called CSI-effect. Professors Steven Smith, Veronica Stinson, and Marc Patry of Saint Mary’s University (Halifax, Nova Scotia) find evidence there is, but they wonder whether the effect is not a juror-problem but rather manifests itself in the ways that attorneys behave. Judge Donald Shelton (also an adjunct professor, Thomas Cooley Law School and Eastern Michigan University) and his colleagues, Professors Gregg Barak and Young Kim (Eastern Michigan University), have found something is going on, but suggest it is a “tech effect” rather than a specific effect of television shows such as CSI or Law and Order. Professors Cole (University of California, Irvine) and Dioso-Villa (Griffith University, Brisbane, Australia) are skeptical but provide valuable guidance for protecting the judicial system against any impacts from real or imagined effects.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; both teacher heads agree on what is shown here.
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".