Preserving, reconstructing and enhancing the place identity: identifying principles of urban sustainability with placemaker method
Bibliographic record
Abstract
The changes that are occurring as a result of mutations in interpersonal and intergenerational relationships, globalisation and new technologies have led to phenomena of homogenisation of territorial specifi cities, but by the same token have also prompted further discussion concerning the importance of identity of place. Present-day urban conditions appear rich in differentiation with regard to the times and ways in which the city is used, for the new typologies of spaces and the change in the modalities of use of those already existing. In order to identify, represent and design the complex transformations in the contemporary city transversal and multi-level methods of analysis and design are being elaborated and experimented capable of representing and making leg-ible the urban complexity that confronts us today. Starting from these premises, the aim of this paper is to illustrate PlaceMaker method of urban analysis and design, whose fl exibility, as is apparent in the three emblematic case studies which will be illustrated, enables it to be used in widely varying contexts and for a range of objectives. In particular, the experiments were carried out in order to identify the current place identity to be preserved, reconstructed and enhanced. The investigation protocol serves as a guide to be adapted and reinforced at some points, according to the characteristics of the place and the intended purposes. The case studies gave rise to 12 principles for place identity enhancement, created from a reasoned set of blueprints for the various experiments carried out using the original PlaceMaker method which conclude the paper.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".