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 X ochimilco in the F ederal D istrict of M exico C ity. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".