Analysis of urban metabolism in an informal settlement using the MuSIASEM method in Lima
Bibliographic record
Abstract
By 2050, 68% of the global population will reside in cities, driving rapid urban growth and intensifying demand for scarce ecological resources within the Water-Food-Energy nexus. Social metabolism quantifies energy and material transformations with a social focus, building upon urban metabolism. Its application in resource-scarce informal settlements (ISs) has the potential to enhance their sustainability significantly. As community dynamics evolve, acknowledging society as a dynamic variable within this framework becomes increasingly relevant. Our study employs the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) framework, focusing on key variables: human activity, land use, money, energy, water, waste, and food. Based on surveys, interviews, GIS datasets, and statistical information, the study investigates the Ciudad de Gosen IS in Lima, Peru. The results show that, in the socio-economic dimension, 43% of the time employed is directed to the unpaid work sector. Notably, 71% of women and 29% of men spend a mean of 44 h/week/person caring for children or elderly. In the paid work sector, there are gender asymmetries; men have a salary 54% higher than women. In the ecological dimension, more than 78% of the homes have access to basic services, unlike other informal settlements in Latin America and Africa. © 2024 The Authors
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".