Toward an integrated theory of spatial morphology and resilient urban systems
Bibliographic record
Abstract
We take the first step in the development of a new field of research with the aim of merging spatial morphology and resilience science.This involves a revisiting and reunderstanding of the meaning of sustainable urban form.We briefly describe the fields of resilience science and spatial morphology.Drawing on a selected set of propositions in both fields, we put urban form in the context of the adaptive renewal cycle, a dynamic framework model used in resilience science to capture the dynamics of complex adaptive systems, of which urban systems are prime examples.We discuss the insights generated in this endeavor, dealing with some key morphological aspects in relation to four key attributes of resilience, i.e., "change," "diversity," "self-organization," and "learning."We discuss and relate these to urban form and other social variables, with special attention paid to the "backloop phase" of the adaptive renewal cycle.We conclude by postulating ways in which resilience thinking could contribute to the development of a new research frontier for addressing designs for resilient urban social-ecological systems, and end by proposing three strategic areas of research in such a field.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".