French cultural wars: public discourses on multiculturalism in France (1995–2013)
Bibliographic record
Abstract
Current literature on multiculturalism is often based on the analysis of national legislation and institutions. But to understand the evolution of multiculturalism, we also have to take into account the various public debates over it. In this article, I analyse how the term ‘multiculturalism’ was used in four French national newspapers from 1995 to 2013. I use critical discourse analysis, which, through the study of vocabulary and the discursive process, allows us to chart the underlying ideologies of the texts. This research modifies the widespread perception that France is an ‘assimilationist’ country. In fact, the philosophical principles of recognition and non-discrimination have grown widespread in intellectual circles: the term ‘multiculturalism’ is used in largely positive fashion in three of the four national newspapers analysed. Criticism of ‘multiculturalism’ must be interpreted as apprehension over the potential consequences of the demographic evolution triggered by post-colonial immigration. The theme of multiculturalism is increasingly present in public debates in France, and has become an entrenched element of the left–right ideological divide. The ideological stances of all the newspapers remained constant over time: we see a sharp dichotomy between two opposing philosophies that reflect different visions of the individual and the larger community.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| 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".