Methodology for Prioritizing Sustainable Urban Regeneration Interventions in Informal Settlements: Case Study in Lima
Bibliographic record
Abstract
Urban areas in low-and middle-income countries are rapidly expanding, leading to a significant proportion of the population living in informal settlements . These settlements are characterized by their socioeconomic disadvantage and being generally located in vulnerable areas along with disconnection from basic services. Urban regeneration projects in informal settlements have increased, however, the absence of a defined method for prioritizing the interventions to be performed has produced a mismatch between the implemented interventions and the local needs. Instead, these interventions tend to respond to preconceived agendas, which, in turn, leads to the creation of unsustainable projects. In this regard, this study proposes a three-phase methodology to prioritize interventions in terms of sustainable urban regeneration in informal settlements. In the first phase, a diagnostic matrix composed of 4 dimensions, 18 variables, and 61 indicators is built based on a literature review of sustainable urban regeneration. In the second phase, an interactive, publicly accessible web instrument is developed to visualize the indicator data for Metropolitan Lima. In the last phase, the instrument is tested, which results in a district-level diagnosis. The proposed methodological approach facilitates objective, rigorous quantitative analysis for local governments to optimize financial resource utilization and facilitate decision-making.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".