Social Mix Policies in the French Eco-Districts: Discourses, Policies and Social Impacts
Bibliographic record
Abstract
In the 1960s, France built large high-rise developments to house poor and immigrant populations. This policy led to the rise of crime and violent unrest in those developments. Responding to that failure, France has tried, especially since the eighties, to promote a social mix policy in its new housing developments. In the first decade of the twenty first century, France elaborated an eco-district (eco-quartier) program whose guidelines emphasize the goals of this social mix policy together with affordability in public social housing. In light of these developments, this paper focuses on the socio-economic aspects of French eco-districts, especially with respect to low-income populations. The eco-quartier housing distribution has shown that social mix goals are barely reached. In affluent cities, where property prices are high (such as Paris, its middle-class suburbs and some large cities), the municipalities build eco-quartiers in substandard neighborhoods, to attract middle class families. In average cities, some municipalities have implemented more social housing than planned, to provide developers with access to State subsidies and loans – but can still privilege the middle-class in the allocation of the resulting housing. In the poorest French towns, eco-quartiers can improve living conditions for local residents but do not effectively promote social mixing.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".