HOW THE WAR WAS ‘ONE’: COUNTERING VIOLENT EXTREMISM AND THE SOCIAL DIMENSIONS OF COUNTER-TERRORISM IN CANADA.
Bibliographic record
Abstract
The current global “war on terror” highlights a fundamental quandary for all liberal democracies seeking to counter the violent extremism of their own citizens while maintaining civic rights and freedoms. This challenge accompanies a transformation in international conflict from inter-state war and superpower rivalry, to homegrown terrorism, radicalization-to-violence, Internet propaganda, and targeting and recruitment of vulnerable persons. These new threats shift the battlefield, as traditionally defined, to the home front, as extremist violence is nurtured by and perpetrated within public spaces, such as schools, places of religious worship, civil society and the home. Today, violence emanates from within liberal democratic society and its extremist motivations bypass the very institutions that would otherwise support civic rights, freedoms and multiculturalism. As such, attempts to counter extremist violence must appeal to the political, social, cultural, religious and familial aspects of human behavior alongside a parallel shift in efforts to keep citizens safe within their own social spaces. In recent years, Canada has been introduced to home grown and lone individual terrorism with the cases of attack against armed forces personnel in Saint-Jean-sur-Richelieu and Ottawa in 2014. This article identifies the social dimensions of counter-terrorism in the Canadian context, a propitious case by which to evaluate different approaches to countering violent extremism. Canadian initiatives - simultaneously proliferating and in their infancy – raise a host of questions about counter-terrorism in liberal democratic countries. For example, why do individuals radicalize-to-violence in rights-based and multicultural societies? How and when can the liberal democratic state best temper the radicalization process in ways that are effective and procedurally just? What state-society balance works best to counter radicalized viewpoints? Who are the appropriate stakeholders in mounting and monitoring counter radicalization programs? What risks accompany government engagement with communities against terrorist activity? And what are the appropriate measures of success? These questions lay the groundwork for an empirical analysis of prevalent programs in Canada against the background of the “war on terror”, multiculturalism, racial profiling, community policing and other contemporary Canadian values.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".