An exploratory media framing of the extradition of a serial rapist: a corpus assessed study in the Malaysian English newspapers
Bibliographic record
Abstract
This paper investigates the role of media framing in shaping public perceptions of crime and criminal behaviour through the analysis of the case of Selva Kumar Subbiah, a serial rapist from Malaysia who targeted young women in Canada, was charged, served time, and extradited to the country of origin. Employing a corpus and computer-assisted textual analysis methodological approach and framing theory, this study examines the discourses in news articles from two national English newspapers (n=27) of the case in 2017 focusing on news sources and used frames. The study also explored the pertinent issues discussed on the Facebook page of the news portals about the news articles using thematic analysis based on 2738 posted comments. The study highlights the importance of understanding media framing and shaping public perception. The analysis suggested that the media framed the extradition through the focus on criminal offender, public safety, legal and law, family, police monitoring, and the victim. The study highlights the importance of understanding media framing in shaping public perceptions of crime and calls for further research to explore the implications of media representations of crime and criminal behaviour. By exploring how the media portrayed Subbiah’s story, this paper seeks to shed light on the complex relationship between media representations of sexual violence and public perception.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".