Struggles over city-making: The community program for neighborhood improvement in Mexico City
Bibliographic record
Abstract
This article traces the trajectory of the making and unmaking of the Community Program for Neighborhood Improvement (PCMB), a participatory program for upgrading social infrastructure and public spaces in marginalized neighborhoods of Mexico City. The PCMB is an example of the range of upgrading programs supported by progressive local governments throughout Latin America over the past 20 years. Proposed by the city’s urban popular movement as part of their longstanding commitment to “city-making from below,” the PCMB was launched in 2007. At its inception, the PCMB was designed to co-produce neighborhood improvements through providing state support for resident-led planning and governance of community spaces. In 2019, the Mexico City government unexpectedly dismantled key participatory elements of the PCMB and folded it into other city priorities, including safe pathways and its surveillance-oriented security strategy. Based on fieldwork involving site visits and interviews with residents, community leaders, and city officials, we narrate the transformation of the PCMB (2007–2021) as state-society struggles over city-making. We argue that these tensions pivot around different spatial and political logics pertaining to territory, agency, and citizenship in city-making. The analysis also brings into focus how local governments attempt to diffuse, co-opt, or contain more radical city-making initiatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".