Discursive constructions of child sexual abuse, conduct, credibility and culpability in trial judgments
Bibliographic record
Abstract
A discourse-analytic approach to the adjudication of child sexual abuse cases is offered as an alternative to conventional social-science theories and methodologies. Previous research on legal decision-making has suffered from (1) its association with a realist epistemology of science, (2) the use of cognitivist deterministic explanations, (3) the use of the apparatus (language) of discrete variables, (4) its view of language as transparent and literal, (5) its failure to recognize the constructed and contested nature of legal discourse. I used discursive psychology, especially its focus on fact construction, to analyze 12 trial judgments in criminal cases involving child sexual abuse in Ontario from 1993 to 1997. Analyses identified the discursive resources used by judges to evaluate the credibility of sexual abuse allegations and of complainants and associated witnesses. Instantiation of the presumption of innocence was reflected partly in judges' orientation to the doubt-constructive properties of complainants' testimony: evidential problems were either discounted or affirmed as a warrant for doubt. Judicial evaluations demonstrated the pragmatic flexibility of discursive category use. For example, the uncertainty displayed by complainants regarding aspects of their allegations could be treated as a warrant for doubt or as an authentic reflection of victims' ignorance about sexual matters. When judges drew on stake and interest to uphold or undermine the credibility of witnesses' accounts, they typically used modalized formats that marked these constructions as conjectures. In contrast, unhedged assertions were used to affirm the reliability of the evidence upon which judges grounded their decisions. Mention of facts (treated as objective evidence) and of the law (treated as legal rules) provided dual warrants for adjudication. Judges' assessments attended not only to concerns about the facticity of evidence and accountability of witnesses, but also to judges' own accountability in constructing their decisions. I discuss the contributions of these findings (and analyses of judicial descriptions of allegations and verdict construction) to research on child sexual abuse and the law. I also address the limitations of this research and the possible ideological implications of methodological relativism and reflexive criticism associated with the study of fact construction.
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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 | 0.029 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".