Bibliographic record
Abstract
In the wake of the January 2015 attacks on Charlie Hebdo, (a satirical newspaper in Paris), there was much commentary on free speech, security, intelligence and the marginalization and dispossession of some young Muslim immigrants (and children of immigrants). Analysis has seemed, reasonably, to focus on economic opportunities, as well as addressing some identity and cultural issues, such as whether or not female Muslims should be allowed to wear a headscarf. The media reported that the two young men who seem to have led the attack on Charlie Hebdo had dropped out from school, going on to work low-wage jobs such as pizza delivery. Little of the news coverage details their education beyond that they were remembered as average Parisian boys, as media coverage focused of course on live breaking events, with less in-depth analysis of the social, economic and political factors driving the broader conflict between European and North American states and their Muslim immigrant residents and citizens. The Charlie Hebdo attacks, like 9/11, were only one expression of this broader escalating and expanding conflict. The academic literature does discuss in depth the history of France’s colonization of countries such as Algeria and Morocco, as well as the racist oppression and economic marginalization Maghrebi immigrant families face—even two generations later. (Bienkowski, 2010; Bowen 2007; Cesari 2002; Franz 2007; Limage 2010; Keaton 2005; Jugé and Perez 2006; Landof and Pagan 2005; Levine 2004; Mahmood 2006; Zimmerman 2015). This paper will thus explore the question of whether there is a “school to terror” pipeline—that is, is there anything about the pedagogy, curriculum, school culture or educational policies of France which might well be contributing to the radicalization of young people? I will conclude with consideration of what peace pedagogy might be able to contribute in terms of conflict transformation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".