Bibliographic record
Abstract
The term “radicalization” has become a hot and sexy topic as of late. Radicalized youth of all genres, be they Jihadist, radical Islamist, Left-wing, Right-wing, White Supremacist; have proven to be of concern to law enforcement, government officials, intelligence groups, and the Diaspora at large. Concerns also resonate in the communities of those affected by the radicalization process. The radicalization of young people, it should be noted, is not a new thing. It has however, garnered International attention in the recent years, partially due to the propensity of the actions of those involved. When looking at the issue of any genre of radicalized youth or of individuals of any age for that matter, it is imperative to look at the etymology of the word and a proper definition of the term. The Merriam-Webster Dictionary (2013) defines radical as having extreme political or social views that are not shared by most people. Its synonyms are extremist, fanatic (or fanatical) rabid, extreme, revolutionary, revolutionist and ultra. The term radicalize has been defined as to make radical, especially in politics. Therefore, radicalization, it is safe to say is the process by which individuals adopt extreme political, social or religious views and ideals. It is the process and by which these individuals (generally youth) implement these ideals and views into their daily lives. The steady rise in radicalized young people on a global scale, along with the changes in social and political environments, as well as, the flourishing of various global groups has been propagated or advanced in many cases through the use of the material housed on the Internet and the dark web. To read and download the complete article you can sign up here for free: http://journals.sfu.ca/jed/index.php/jex/user/register
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.038 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".