Technologies and collaborative services proximity in the smart cities: Distributed ledger as a push for new relationships
Bibliographic record
Abstract
The expected demographic densification presents specific critical points where op-portunities for improving citizens' lives can be identified. For this reason, projects are underway to analyze and explore the dynamics of cities to adapt to new con-texts. Several European cities, including Milan, Paris, and Barcelona, are already implementing changes to encourage new types of neighborhood organizations which revolve around the concept of proximity, and primary services close to home. In this context, it seems fundamental to seek connectivity, encouraging new forms of relationships between citizens. The use of new digital tools, such as blockchain, favors new types of autonomous organizations that can manage activities on a neighborhood scale. Design should propose suitable and innovative models of ap-plication and act as a facilitator for their implementation. Through design, it is also possible to identify guidelines for the relationships in a neighborhood and to define activities and experiences with which citizens can relate.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".