Press coverage of lone-actor terrorism in the UK and Denmark: shaping the reactions of the public, affected communities and copycat attackers
Bibliographic record
Abstract
Following 9/11, Al-Qaeda-orchestrated plots were considered the greatest threat to Western security and sparked the coalition’s war on terror. Close to a decade later, the post-9/11 threat landscape had shifted significantly, leading then CIA-director Leon Panetta to describe “the lone-wolf strategy” as the main threat to the United States. Subsequent lone-actor attacks across the West, including the cities of London, Nice, Berlin, Stockholm, Ottawa and Charleston, further entrenched perspectives of a transformed security landscape in the “after, after-9/11” world. The unique features of lone-actor terrorism, including the challenges of interdiction and potential of copycat attacks, mean that the media is likely to play a particularly important role in shaping the reactions of the public, affected communities and copycat attackers. This article presents findings from a content analysis of British and Danish newspaper reporting of lone-actor terrorism between January 2010 and February 2015. The study highlights that lone-actor terrorism is framed, with national variations, as a significant and increasing problem in both countries; that Islamist lone-actors are often represented as distinct from far-right lone-actors; and that some reporting, despite relatively limited amplification of specific terrorist messages, potentially aids lone-actors by detailing state vulnerabilities to attacks.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".