Self-Injury in the News: A Content Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI) has garnered increasing academic and media attention in society. While more awareness of NSSI is welcomed, inappropriate reporting of NSSI in media could heighten the potential for stigmatization and misunderstanding of NSSI and people who engage in it. Further, certain kinds of content (e.g., graphic imagery) may be harmful to people who self-injure (e.g., provoking urges to self-injure). These concerns notwithstanding, little research has focused on how NSSI has been portrayed in news media. Such knowledge would therefore represent a first step toward illuminating the nature of media depictions of NSSI and highlight potential areas to circumvent any concerns. Using content analysis, we explored how NSSI was portrayed in 568 online news articles about NSSI, published between 2007 and 2018, from top news sources in Australia, Canada, New Zealand, the United Kingdom, and the United States. Codes were developed based on prior research investigating online NSSI content, and the available existing and proposed media guidelines for the reporting of NSSI at the time of the study. While the overall tone of the examined articles was often neutral, areas of concern included: most articles detailing specific NSSI methods, the frequent inclusion of negative imagery, an absence of clear communication about what NSSI is and why people self-injure, the use of sensationalist and stigmatizing language, and a lack of helpful resources. These preliminary findings suggest the utility of a set of newly developed media guidelines on the reporting of NSSI as one component in an effort to address the stigmatization and misunderstanding of NSSI and individuals who self-injure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it