Victim Impact Statements, New Media Technologies, and the Classical Rhetoric of Sincerity
Bibliographic record
Abstract
Long a concern for their interjection of emotional appeals into the sentencing process, victim impact statements (VISs) have recently become even more worrisome to some critics, who see in the growing availability of sophisticated video-editing technology the capacity for even unskilled users to construct testimony in ways that corrupt rather than enable reasoned judgment. Drawing upon a combined analysis of the U.S. Supreme Court case Kelly v. California and classical rhetorical theory, I argue by contrast that, especially in death penalty cases, a jury’s demand for authenticity in performance mitigates the potential impact of such technology precisely because the cultural construction of emotive authenticity requires an outpouring of feeling whose very lack of control testifies to its genuineness. As an artistic and compositional techne, the capacity of new media technologies to evoke, seemingly effortlessly, complex representations of emotional states thus contains within itself a fundamental limitation on its power. Recognizing the importance of authenticity and sincerity also reveals a key element of why the introduction of a VIS remains an attractive option for the prosecution, for in allowing the introduction of a VIS the state secures for itself a rhetoric of sincerity that as an institution it could not otherwise hope to wield.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".