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Record W2293773714 · doi:10.5539/mas.v10n3p89

The Roles of Neighborhood Cultural Spaces in the Development of Citizenship Culture

2016· article· en· W2293773714 on OpenAlexvenueno aff
Maliheh Izadi, Jamal Mohammadi

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipThrivingSociologyUrban cultureSpace (punctuation)Meaning (existential)Environmental ethicsSocial sciencePolitical scienceEpistemologyPoliticsLinguisticsLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.293
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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