Exploring the urban systemic scenarios of improving socioecological conditions in an informal settlement of a developing country with a system dynamics model
Bibliographic record
Abstract
Informal settlements (IS) present a complex system of social, economic, and ecological interactions that arise spontaneously and unplanned in urban areas and require a forward-looking and comprehensive approach to address their socio-ecological interactions. Moreover, an IS is conceptually considered a sub-system within a broader urban system, interacting with and influenced by internal and external factors. This study aims to model these interactions and factors using System Dynamics (SD), with the objective of simulating and evaluating decisions, actions, and their dynamic consequences concerning settlement consolidation (e.g., enhancing and optimizing urban areas) and improving women's access to formal employment opportunities. Consequently, a 10 year framework SD model for five scenarios was developed, including: (S0) Business as Usual (BAU); (S1) Time quality scenario; (S2) Women in formal employment scenario; (S3) Circularity scenario; and (S4) Comprehensive scenario. The results indicate that the implementation of individual solutions, such as improving the quality of men's working time (without overtime), formal employment for women (equalizing income conditions with men), water circularity (use of fog catchers and the recycling of greywater as a supply), and organic waste management (organic compost for urban gardens and the implementation of urban agriculture), does not fully leverage potential synergies. However, a comprehensive scenario that combines individual solutions jointly achieves a decrease in the time needed to improve women's conditions in formal employability (47.5 %), which is related to the settlement consolidation process. These findings provide insight into possible action strategies and policy implications for effectively addressing the challenges associated with informal settlements. • Systems dynamics effectively uncovers interdependencies and causal relationships. • Comprehensive strategies drive sustainable development in informal settlements. • Synergistic solutions optimize socioecological conditions in informal settlements. • Coordinated approaches empower women through formal employment opportunities. • Valuable insights guide evidence-based policies for transformative change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".