TOWARD A FRAMEWORK UNDERSTANDING OF ONLINE PROGRAMS FOR COUNTERING VIOLENT EXTREMISM
Bibliographic record
Abstract
There is an emerging consensus that ideologically-based narratives play a central role in encouraging and sustaining radicalization to violence, and that preventing, arresting, or reversing radicalization requires some means by which to address the effects of these narratives. Countering violent extremism (CVE) is a broad umbrella phrase that covers a wide array of approaches that have been advanced to reduce the radicalizing effects of extremist narratives. There is considerably less agreement, however, regarding the most appropriate means by which the mitigation of extremist narratives might best be accomplished. An important emerging area of interest is the role of the Internet, both as a forum through which narratives are transmitted and as an avenue for delivering CVE programs. At present, very little is known about which principles and practices should inform online CVE initiatives. This study attempts to establish a foundation and framework for these programs: first, by identifying the concepts and constructs which may be most relevant to countering violent extremism online, and second, by examining the available material from six online CVE programs in relation to these concepts. This examination suggests that these programs are lacking strong theoretical foundations and do not address important elements of radicalization, such as contextual factors or identity issues. It is important that future iterations of CVE programs consider not just the specific content of the narratives, but also take into account why these narratives have resonance for particular individuals.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".