What role do French society and its education system play in promoting violent radicalization processes?
Bibliographic record
Abstract
Radicalization is a complicated phenomenon, which is caused by multiple factors, including poor housing, low education, and unemployment, according to a study by the French Institute of International Relations (Hecker, 2018; Macaluso, 2016). France has a high number of radicalized terrorists, most of whom are homegrown, often with strong cultural ties to former French colonial countries in North Africa (IFRI, 2018). This paper aims to illustrate how social exclusion and marginalization created and perpetuated by the inequalities in the French society and education system (Bourdieu, 1971; Croizet et al, 2019; Goodman 2019; Jetten et al., 2020; Vanten, 2016), may be contributing to the radicalization of many young French citizens. This push factor could be a key precondition for radicalization in many Western societies (Ghosh et al., 2016). While critiquing the French education system, this study insists that schools can and must create a sense of connection with their students and construct resilient and inclusive communities (da Silva, 2017, Ghosh et al., 2017; OECD, 2012). Finally, some pedagogical approaches, especially care in education, are suggested for educational institutions and school agents to effectively build a sense of belonging among young students that would enhance their resilience against radicalization.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".