Bibliographic record
Abstract
The new urban features are not easily identifiable and cannot be easily represented through traditional cartography and tools of representation.In order to explain such new sites and give new terms, several researchers have tested new methodologies, maps, multimedia images, hypertext and software that can render this complexity and permit readability.Starting from those premises, the aim of this study, carried out in the framework of a convention between Consiglio Nazionale delle Ricerche and Dipartimento di Progettazione Urbana e di Urbanistica, Università di Napoli Federico II, is to illustrate new methodological approaches for analysing and representing contemporary urban landscapes.In particular, in the context of the complex-sensitive approach and PlaceMaker method of analysis, a new software tool, currently under development, is presented.The PlaceMaker method identifies the elements of the urban landscape which contribute to the identification of places and are able to influence the cultural and sustainable city construction; such elements and the complexity of the places are represented in a complex map.Experimentation of the method has shown the necessity of the proposed PlaceMaker software tool, which is able to support the collection and management of a multimedia database, the implementation of the phases of PlaceMaker method, the construction of the interactive complex map, and the calculation of indices useful for the project of sustainable urban landscapes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".