Canadian press coverage of terrorism: The virgin terrorist vs. the Canadian jihadist?
Bibliographic record
Abstract
The violent and deadly rise of Incel attacks has disrupted Canada's domestic terrorism landscape by putting a spotlight on non-Jihadist extremism. In 2019, the Canadian Security Intelligence Service released a report which revealed an evolved overview of Canada's domestic terrorism landscape that redefined extremism to include: Ideologically-motivated violent extremism (IMVE). With the occurrence of four deadly Incel terrorist attacks between 2018 and 2020, Canada has experienced more Incel terrorism related deaths than those committed by their Jihadist counterparts. By examining the nexus between news media framings and terrorism, this research study seeks to contribute to the gap in terrorism studies on Incels and analyses the way in which the Canadian news media frames their attacks within the label of terrorism. Referencing Azeezah Kanji's 2018 study of news media framings of Muslim violence as a theoretical and thematic framework, this study employs a qualitative research design to answer the research question: In what ways does news media coverage of Incel attacks compare to the coverage of domestic Jihadist attacks? This study employs a combination of content and thematic analysis to a corpus of news media articles on four recent Incel attacks and compares its findings to Kanji's 2018 study....
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".