The 60 Days of PVE Campaign: Lessons on Organizing an Online, Peer-to-Peer, Counter-radicalization Program
Bibliographic record
Abstract
Combatting violent extremism can involve organizing Peer-to-Peer (P2P) preventing violent extremism (PVE) programs and social media campaigns. While hundreds of PVE campaigns have been launched around the world in recent months and years, very few of these campaigns have actually been reviewed, analyzed, or assessed in any systematic way. Metrics of success and failure have yet to be fully developed, and very little is publically known as to what might differentiate a great and successful P2P campaign from a mediocre one. This article will provide first-hand insight on orchestrating a publically funded, university-based, online, peer-to-peer PVE campaign – 60 Days of PVE – based on the experience of a group of Canadian graduate students. The article provides an account of the group’s approach to PVE. It highlights the entirety of the group’s campaign, from theory and conceptualization to branding, media strategy, and evaluation, and describes the campaign’s core objectives and implementation. The article also analyzes the campaign’s digital footprint and reach using data gleamed from social media. Finally, the article discusses the challenges and difficulties the group faced in running their campaign, lessons that are pertinent for others contemplating a similar endeavour.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".