Bibliographic record
Abstract
The riots that shook the French banlieues in 2005, while unique in their geographic extension and political resonance, are but the most recent manifestation of an ongoing escalation of violence and repression that has periodically rocked the economically devastated, socially fractured and highly cosmopolitan cityscape of post-industrial France. The stigmatization of unemployed youths and outcast working-class families as ‘foreign’ is a complex and multi-layered phenomenon. This article traces the history of the so-called ‘immigrant problem’, and of policy responses to it, from the time of the Algerian war to the republican nationalist backlash against multiculturalism over the past two decades. The trauma of decolonization, increased visibility of Maghrebi, West African, Antillian and other communities with origins outside of Europe, fears of ‘islamicization’, and political/ideological controversies over how the nation's history should be remembered and taught to future generations, have weighed heavily on the representation of immigrants and their descendants as unassimilated threats to national cohesion. Far from limiting their agency to criminality and random social violence, the youths of the banlieues have played an active role in redefining the terms in which citizenship and national identity, as well as the colonial heritage of France, are cast in the arena of public debate, challenging state policies and well-entrenched historical myths in the process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".