The ‘Graying’ of ‘Green’ Zones: Spatial Governance and Irregular Settlement in <scp>X</scp>ochimilco, <scp>M</scp>exico <scp>C</scp>ity
Bibliographic record
Abstract
Abstract This article details the evolving social and spatial dynamics of a planning approach that is now being used to regulate irregular or informal settlements in the conservation zone of Xochimilco in the Federal District of Mexico City. As part of the elaboration of ‘normative’ planning policies and practices, this approach counts, maps and then classifies irregular settlements into different categories with distinct land‐use regularization possibilities. These spatial calculations establish a continuum of ‘gray’ spaces, placing many settlements in a kind of planning limbo on so‐called ‘green’ conservation land. The research suggests that these spatial calculations are now an important part of enacting land‐use planning and presenting a useful ‘technical’ veneer through which the state negotiates competing claims to space. Based on a case study of an irregular settlement, the article examines how the state is implicated in the production and regulation of irregularity as part of a larger strategy of spatial governance. The research explores how planning ‘knowledges’ and ‘techniques’ help to create fragmented but ‘governable’ spaces that force communities to compete for land‐use regularization. The analysis raises questions about the conception of informality as something that, among other things, simply takes place outside of the formal planning system.
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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 | 0.002 | 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 | 0.004 | 0.023 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".