The Roles of Neighborhood Cultural Spaces in the Development of Citizenship Culture
Bibliographic record
Abstract
Cultural space constitutes the physical, cultural and perceptual attributes of a place that creates social phenomenon and place meaning. Thus, this paper discusses the roles of cultural spaces that Cultural spaces in neighborhoods as parts of thriving urban spaces are considered as the most potential urban spaces in the development of citizenship culture due to enjoying potentials and capacities. This study will look into how such cultural characteristics have influenced the revitalization of the local culture. Cultural spaces are considered as one set of the main instruments of cultural development in current societies and are burdened significant responsibility in developing human forces. Cultural spaces, as one set of the main important institutions for cultural services, have important functions in increasing the literacy and culture level of societies. Then establishment of such spaces and the mode of their distribution in the neighborhoods are, directly or indirectly, effective on the degree of individuals’ reference and use of these spaces. The aim of the present study is to identify and analyze spatially the performance of cultural spaces of neighborhoods in the enhancement of citizenship culture. The results of the present study indicate that the development of cultural spaces results in decreasing the differences in urban culture and promoting appropriate citizenship behaviors and consequently, accessing citizenship culture development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".