CHANGES IN SPATIAL PATTERNS OF THE IMMIGRANT POPULATION OF A SOUTHERN EUROPEAN METROPOLIS: THE CASE OF THE BARCELONA METROPOLITAN AREA (2001–2008)
Bibliographic record
Abstract
ABSTRACT To date, the evolution of residential segregation patterns in southern European cities has received little attention. The case of Barcelona metropolitan area (BMA) is particularly relevant following a rapid growth of immigrant population over a very short period. This empirical study seeks to see if this rapid growth has changed the spatial segregation patterns of immigrant groups and led to the emergence of ethnic enclaves. First, we calculate five indices according to the classic dimensions of residential segregation. Then, we use an adapted version of the Poulsen et al. typology in order to identify ethnic enclaves. In the period under study, there was a general increase in two dimensions: Exposure and clustering. There was a decline in evenness and concentration, and a variety of situations in terms of centralization. The evolution of the segregation indices indicates a diversity of segregation patterns: an increase of segregation according to exposure and clustering dimensions, a decline in evenness and concentration, and a variety of situations in terms of centralization. Finally, the findings confirm the presence of ethnic enclaves following seven years of growth in the immigrant population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".