VOICES AGAINST EXTREMISM: A CASE STUDY OF A COMMUNITY-BASED CVE COUNTER-NARRATIVE CAMPAIGN
Bibliographic record
Abstract
This article presents a case study of the recently conceived and ongoing counter-extremism campaign, Voices Against Extremism, a campaign designed and implemented by university students from Vancouver, Canada. Through a multifaceted approach that includes extensive use of social media, academic research, and grassroots community activities and involvement, Voices Against Extremism operates under the mission statement of countering and preventing violent extremism and radicalization through the humanization of minority groups and through the education and engagement of the silent majority. This article examines the effectiveness of this campaign as a proactive counter-radicalization strategy by outlining its specific components and activities. Based on the results of this campaign, suggestions are then offered regarding specific counter-extremism and counter-radicalizations policies that may be adopted by law enforcement, policymakers – or any other organizations concerned with countering and preventing radicalization and violent extremism – with a specific focus on the potential benefits of proactive and long-term social and community engagement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".