Displacement in “actually existing” racial neoliberalism: refugee governance in Paris
Bibliographic record
Abstract
3.5 million people currently live without adequate housing in France with some 10 million others in sub-standard accommodations without secure and affordable rental tenure. In Paris, homelessness has increased a staggering 84 percent since 2005 due to cuts in social service expenditure and the downloading of poverty management onto cities and civil society organisations. Since 2015, the European Union has seen a large influx of refugees from protracted conflicts in the Middle East and North Africa—commonly, and problematically, referred to as the European migration crisis. Although France has amongst the highest rates of refugee application rejections in Western Europe, Paris is increasingly becoming a hotspot for displaced people who are fleeing improper treatment in frontier states. The Paris case, as suggested here, illustrates ‘actually existing’ racial neoliberalism pointing to both the material and ideological features of refugee marginalization. The purpose of this article is two-fold: First, it highlights the various issues of political and shelter-based survival for urban refugees—an aspect understudied especially in cities in the global North. Second, the article aims to overlay pre-existing crises of homelessness, inadequate housing, and poverty with the racialization of refugees within the European migration crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".