Mapping urban morphology: a classification scheme for interpreting contributions to the study of urban form
Bibliographic record
Abstract
Urban morphology is a thriving field of enquiry involving researchers from a wide diversity of disciplinary, linguistic and cultural backgrounds. While this diversity has helped advance our understanding of the complexity of urban form, confusion and controversy has also arisen over the various theoretical formulations forwarded by researchers from different philosophical and epistemological backgrounds. With the aim of improving intelligibility in the field, this paper proposes a straightforward scheme to identify, classify and interpret, or ‘map’, individual contributions to the study of urban form according to their respective theoretical or epistemological perspectives. Drawing upon epistemological discussions familiar to the readers of this journal, the authors first distinguish between cognitive and normative studies. A second distinction is made between internalist studies that consider urban form as a relatively independent system, and externalist studies in which urban form stands as a passive product of various external determinants. Using these basic criteria, it is possible to interpret and synthesize a multitude of contributions and map them using a simple Cartesian grid. The paper highlights how contributions from seemingly different theoretical approaches to urban morphology are intrinsically similar in their treatment of urban form as an object of enquiry.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".