Chapter 10: MEXICO CITY: CHANGING PARADIGMS OF URBANISATION
Bibliographic record
Abstract
Multilayered patchwork urbanisation Airport Urban footprintParis and Saint-Denis have been structuring the region since the 12 th century, Versailles has done so since the 17 th century Includes the La Défense business district, the airport business hubs, shopping malls, the centres of the villes nouvelles, the amusement park Eurodisney, the technopole Saclay and the Plaine Saint-Denis Densification of classic bourgeois neighbourhoods in the west of Paris, around Versailles and in former rural areas Longstanding processes of accumulation of wealth in morphologically diverse residential areas composed of dense urban neighbourhoods, zones with detached houses and villages in the urban periphery Longstanding process of reinvestment and upgrading of neighbourhoods in the city of Paris and the banlieue, often accompanied by radical transformation of their social composition and urban morphology Transformation of parts of the banlieue, leading to social, functional and morphological heterogeneity; resistance to rapid embourgeoisement due to the high number of existing grands ensembles Concentration of poverty and racialised peripheralisation in the fragmented and heterogeneous urban fabric of the northern and western parts of the red belt around the city of Paris A large-scale process of urban restructuring resulting in a patchwork of urban fragments with very different histories, dynamics, logics and functions
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".