Prioritizing Effective Factors on Liveliness and Improvement of the Urban Life Caused by the Development of Green Spaces with the Attraction-Repulsion Pattern
Bibliographic record
Abstract
The daily increase in population and the complexity of urban issues, shortages in suitable financial and human resources, environmental pollutions, etc. sometimes cause the citizens to forget or be unable to fulfill their needs in the hobnob of life, pollution, tiredness and the routine of life. This has led some factors such as the closeness to the work and living place of human beings to nature, small green spaces within the cities and their benefits for the people receive less attention in our time. Cities, as centers of man's activities and life, in order to keep their sustainability have no way but to accept the structure and they have no function affected by natural systems. Here, urban green spaces, as the vital and inseparable part of the cities' unified form in their metabolism, have basic roles and their shortage can cause serious disorders in the lives of the cities. Public green spaces have a significant impact in improving the life quality of the citizens, liveliness and the beautification of the city. With regard to these issues, it has been tried in this paper to analyze the mental and social impacts of urban green spaces on the improvement of the citizens' life quality and their roles in the beautification of urban spaces and their liveliness by using the attraction-repulsion pattern with an approach to green spaces and by analyzing case studies among the citizens. The results indicated that the citizens use green spaces mostly to have access to clean air, family entertainment, liveliness, being away from the pollutions and the smallness of their houses, walking, relieving their tiredness, running away from their routine lives, etc. and these spaces have significant impact in the beautification of urban environments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".