Neglected? Strengthening the Morphological Study of Informal Settlements
Bibliographic record
Abstract
Methods of articulating the morphological structure of slums can have considerable potential in better planning for site-specific design or policy responses for these areas in the contemporary city. Although urban morphology traditionally studies landscapes as stratified residues with distinct divisions between lot and boundary, built and unbuilt, the authors find these definitions insufficient to address the complexity of slum morphology. Through this article, the authors’ identify that morphological analysis of informal settlements needs to be sensitive to the dynamics and the absence (or blurring) of physical boundaries. By analyzing the spatial impact of social, economic, and political factors, situational and site factors, building typologies, and configurations of circulation space, an attempt to articulate the morphological structure of slums is made. Aiming to overcome the current polarization in the literature between the formal and informal city, this article adds to the ongoing research on the study of challenges within contemporary cities, by providing new methodologies for studying the morphology of slum urbanization and shaping planning practice.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".