Play-by-Play Justice: Tweeting Criminal Trials in the Digital Age
Bibliographic record
Abstract
Abstract Journalists routinely live-tweet high-profile criminal trials, a practice that raises questions about access to justice and the principle of open court. Does social media open up the justice system? There is a normative debate in the literature about the use of Twitter and social media in the courtroom. This paper takes on this debate by exploring the relationship between digital technologies and criminal justice. Through a systematic examination of journalists’ tweets during two key trials (Ghomeshi and Saretzky), we ask to what extent can the live-tweeting of court proceedings achieve greater access to justice in Canada? We argue that while the live-tweeting does provide more access to court, potentially furthering the principle of open court, the nature of this access provides little in the way of increased engagement with the public and its understanding of the legal system. This paper makes contributions to both the legal studies and digital politics literatures.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".