Bibliographic record
Abstract
Radicalization is the transition into acceptance and approval of extremist beliefs and actions, including condoning or committing acts of violence. In recent decades, the internet has played a crucial role in the radicalization of extremists and terrorists, as well as facilitating radical groups' recruitment efforts. The present review briefly discusses what radicalization is and how it unfolds in a general sense, before exploring how the internet is involved in three kinds of radicalization. The first is the deliberate radicalization and recruitment of new members into formally organized extremist groups (e.g. white supremacist militias and radical Islamic terror groups), and the second is self-radicalization via the internet, wherein unstable, discontent, and/or disenfranchised individuals pursue increasingly radical ideas and communities online until they condone or commit acts of violence on their own, without formal membership into an organized group. The third type of radicalization explored is stochastic or probabilistic radicalization, in which individuals encounter seemingly or actually benign ideas, beliefs, and pundits online, and are slowly radicalized via increasingly bold and dramatic content being suggested by the recommendation algorithms of Google and Youtube. The review clarifies some distinctions between the three types, before a brief summary and discussion. Content warnings: discussions of violence, bigotry, and hate.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".