Digitization of Cities and Its Impact on City Sustainability
Bibliographic record
Abstract
The study examines the critical challenges of rising traffic congestion and environmental pollution in Baghdad, practically in the Karrada district, driven by rapid pollution growth and economic expansion.It evaluates the role of smart urban services in addressing these issues, focusing on their advantages and their potential drawbacks.The research hypothesis that electronic services can significantly reduce environmental pollution by minimising traffic congestion and individual trips, leading to lower carbon emissions.However, it also highlights potential challenges, such as the complexity of using digital tools and the strain on network infrastructure.Using a descriptive and deductive methodology, the study analysis the practical effects of smart urban services in Karrada.The findings confirm the hypothesis, demonstrating that these services enhance residents' quality of life by improving citizen satisfaction, streamlining communication, and increasing the efficiency of public services through egovernment platforms.Despite these successes, the study identifies critical obstacles, including limited access to technology, network overload, user difficulties, and concerns over data security.These insights underscore the need for balanced strategies to maximise the benefits of smart urban solutions while addressing their challenges, contributing to sustainable urban development.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".