Bibliographic record
Abstract
Abstract This article considers the use of references to the American ghettos in the history of the French working‐class suburbs ‐ the “banlieues”. When construction work first began on the new housing developments, concerns were voiced about the scale and isolation of their apartment blocks, in contrast to the model of individual houses. With the pauperisation of a section of the working classes and growth of the immigrant population, successive periods of rioting and the prevalence of an economy of drug dealing and other illicit trades among an element of the youth in these neighbourhoods, over a quarter of whom are unemployed, the comparison between ghettos and banlieues gained credibility. Also influential was public policy targeting the French banlieues, which reflected the dogma of republican equality, and fierce opposition to communalism. However, a section of the inhabitants of these neighbourhoods have gradually developed a subjective feeling of being separate from the rest of society and imprisoned in a ghetto with its own codes and laws, for example in the treatment of women. So the clear distinction established 20 years ago between the “black belt” and the 𠄌red belt” (Wacquant 2006) has greatly reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".