(Re)constructing Informality and “Doing Regularization” in the Conservation Zone of Mexico City
Bibliographic record
Abstract
This paper examines the introduction of land-use planning requirements into the regularization process of informal settlements in areas designated as “conservation land” in Mexico City. Since 1997, the government has increasingly deployed digital technologies to map and track informal settlement in conservation land in order to select those eligible for reclassification as “residential land use”: a prerequisite for other stages in the regularization process, including property titling, access to urban services and subsidised loans for home improvements. We argue that the incorporation of land use planning into the discursive and material enactments of regularization continues to reproduce the social class divisions behind the otherwise rather tenuous distinction between formal and informal urban development. Although presented as a technical concern by planners, regularization remains embedded in political processes and outcomes, a characteristic long recognised in the abundant literature on the subject. What is new is the geo-referencing of informality as part of land use planning, as this alters the dynamics of regularization processes, now involving the everyday planning practices of local government. This experience thus suggests the need for re-conceptualising informality as a form of selective spatial regulation and governance integral to the planning and urban development process.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".