Stories of violence and dementia in mainstream news media: Applying a citizenship perspective
Bibliographic record
Abstract
In this article, we analyze how mainstream news media frames violence in relation to dementia and the consequences of different frames for people living with dementia and their carers. Conceptually, the goal is to bring literature on citizenship and aggression into dialog with each other. Empirically, a total of 141 regional and national English-language mainstream Canadian news media articles (2008-2019) with a focus on dementia, violence, and aggression were analyzed. Analytically, we examine how different actors are portrayed as victims or perpetrators; how their histories (identities, belonging, and exclusion) are told; how dementia is used to explain events; and what types of expert knowledge and authorities are introduced to make sense of stories of violence in relationships of care. Our analysis points to the implications of media narratives for people with dementia as well as carers and researchers seeking to address stigma and call for change.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".