Faces of the Courtroom: How the Visual Elements of Portraiture Contribute to the Construction and Communication of Courtroom Sketches
Bibliographic record
Abstract
The purpose of this study is to interrogate the composition and influence of courtroom sketches from an art historical perspective with the aim of demonstrating not only their importance in the public’s understanding of the Canadian legal system, but also their ability to shape and challenge our perception of the individuals that appear before the courts. Specifically, this study focuses on the ways that art historical portrait theory can contextualize the construction and legibility of a courtroom sketch. The question of how courtroom sketches use the visual elements of portraiture to represent participants at a trial is applied to three case studies: the sentencing hearing of former colonel Russell Williams, the trial of Jian Ghomeshi, and the trial of Senator Mike Duffy. Each case took place in the Ontario criminal justice system and produced significant media attention resulting in a large number of sketches from various artists. The correlations identified and discussed in this study between the courtroom sketch and the practices of portraiture demonstrate that the historical conventions of representations are inextricable from the construction of images in contemporary media. In addition, this analysis serves to extricate the courtroom sketch from its traditional role as support for a textual report of legal events to a more accurate role as a primary source of information dissemination to the public.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".