Contesting the text: Canadian media depictions of the conflation of mental illness and criminality
Bibliographic record
Abstract
Abstract Researchers have identified that portrayals of mentally ill people as violent and criminal are among the most common depictions of mental illness in the popular media ( Nunnally 1961 , Wahl and Roth 1982 , Day and Page 1986 ). Little attention, however, has been paid to assessing the textual strategies whereby such representations gain currency. This research is interested in investigating the reporting techniques utilised by the popular press including the ways in which power, knowledge and ideology articulates in and through media reports about mental illness. This study draws on Foucault (1972 ) and van Dijk (1998 ), to assess data generated out of a discourse analysis of 195 articles from two major Canadian newspapers over the past decade (1990–1999). Findings suggest that the linkages between criminality and mental illness are achieved through the use of ideological, polarised talk that creates distinctions between Us and Them, as well as through a hierarchy of mental illness. Within the hierarchy of illness, three portrayals are explored including the mentally ill criminal, the passive patient and class based illness depiction. A major finding reveals that, throughout the various representations, a central reporting feature is of mentally ill people as simultaneously rational and irrational. Furthermore, varying degrees of agency are afforded mentally ill subjects on class lines, which has implications for the substantiation of responsibility and blame.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".