Schizophrenia: Proportionality and erasure in Canadian news media
Bibliographic record
Abstract
This mixed-method study analyzes reports related to schizophrenia in Canadian news media during the calendar year 2022 ( N = 237). The corpus was coded for tone, journalism sources and themes. Correlations between those elements were measured, and also compared against a baseline of random articles from the same database and time period. A variety of Welch’s t-tests suggest that news about this severe mental illness is negative in tone 63% of the time, and linked to themes of violence and criminality at twice the rate of the baseline corpus. Sources such as police, lawyers and others from the legal system dominate the articles by a wide margin in absolute and relative terms compared to the baseline. Organizational sources, such as advocacy groups, correlate to the minority of reports with a positive tone. Those living with schizophrenia or their families are quoted more frequently compared to the same kinds of voices in the baseline, but they do not result in positive tone. Political sources are under-represented in the corpus; reports related to the themes of resources and health care funding are coded at the lowest frequency. The data is considered in the context of journalism practice related to sourcing, its style guides and ethics guidance such as truth-seeking and proportionality, but also the post-structural theory of erasure as an explanatory gesture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".