The criminal justice system on trial: Shaming, outrage, and gendered tensions in public responses to the Jian Ghomeshi verdict
Bibliographic record
Abstract
Drawing on an affective framework, this qualitative content analysis of the immediate public responses on Twitter in the hours following Jian Ghomeshi’s not guilty verdict (n = 3943 tweets) reveals two key discourses of public opinion. Twitter users depicted the criminal justice system (CJS) as having worked and/or failed, and these intensifying divisions were highly gendered. Members of the public pitted notions of the “rational male” against the “emotional female”, and these debates heavily supported or opposed a patriarchal legal rationality. This study sheds light on the ways in which adversarial justice systems reproduce adversarial discourses on crime, and overlook the problems entangled in misleading applications of rationality to sexual consent. The wide circulation of blame to all parties involved in this case leads us to the conclusion that the CJS, in its current punitive form, does not instil a sense of confidence in the public. With a shifted focus on the healing and dignity of everyone involved in sexual assault cases, we recommend Restorative and Transformative approaches to justice as alternative measures to respond to sexual assault.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".