Minga as a placemaking tool in peripheral neighbourhoods. Co-design experience in Calderon, Quito <sup>*</sup>
Bibliographic record
Abstract
This article explores the role of local vernacular socio-spatial practices in co-design and placemaking in Calderon, a peripheral urban parish in Quito, Ecuador. Using a case-study method and transformative paradigm theory, the research employs a mixed-methods approach for data collection and analysis. The focus is on ‘Minga’, a collaborative community effort manifested in two forms: ‘community mingas’ organised autonomously by communities, and ‘megamingas’ coordinated by public institutions. Community mingas enhance residents’ sense of belonging and pride, empowering them in shaping public spaces through a bottom-up approach. In contrast, megamingas exhibit top-down organisation with limited community engagement, raising concerns about social impacts. The study underscores mingas’ potential for social cohesion, cultural expression, and sustainable development in urban design. It emphasises the nuanced understanding needed for fostering active participation and addresses challenges such as sustaining community ownership. Despite yielding immediate benefits in public space enhancement, sustaining long-term engagement is crucial. The study concludes that mingas offer a valuable avenue for residents to actively contribute to public space improvement, fostering shared responsibility and long-term sustainable development in low-income neighbourhoods.
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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 | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".