Smart Cities and Urban Mobility for Innovation and Sustainability
Bibliographic record
Abstract
This study is grounded in theories of degrowth and the circular economy. The general objective of this research leads to a thematic discussion of urban mobility in big cities that will suggest solutions for implementation in sustainable forms of technology which could transform metropolises into smart cities. As specific objectives, it seeks (1) to demonstrate the problem of urban mobility in large cities; (2) to address the global trend of changing unsustainable consumption habits into habits of minimal consumption, linked to Latouche's theory of degrowth and the circular economy; and (3) to interpret new trends in large urban centers with a focus on the smart city. As it happens, young millennials choose to enjoy goods, instead of owning them; with the availability of the Internet, people have started to interact with cities, exchanging information about traffic (congestion and public transport routes); they also access alternative modes of transport, such as the use of shared vehicles, bicycles, electric scooters and other modes that can be rented through apps. However, many companies which claim to offer sharing services in fact practice pseudo-sharing for the sake of profit. The methodology in this study was inductive in nature, using secondary sources, from qualitative bibliographic research starting from electronic searches of articles available in public domain pages, which allowed various publications on the chosen topic to be accessed for review. The result is an academic contribution to the public understanding of current conditions that may lead the elaboration of public policies for updated scenarios of smart cities to the benefit of residents.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| 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".