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Record W2795204543 · doi:10.5539/jsd.v11n2p34

The Neighborhood-School Characteristics: As an Effective Factor of Social Sustainability in Neighborhood

2018· article· en· W2795204543 on OpenAlexvenueno aff
Parisa Ziaesaeidi

Bibliographic record

VenueJournal of Sustainable Development · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSocial harmonyFeelingAffect (linguistics)SustainabilityHarmony (color)Context (archaeology)Quality (philosophy)Unit (ring theory)PsychologySocioeconomicsGeographySociologySocial psychologyMathematics education

Abstract

fetched live from OpenAlex

The neighborhood unit promotes quality of life, community feeling, and well-being by providing facilities. Sharing the main facilities of the neighborhood with all residents can play an important role in the satisfaction of the neighborhood. When a school (as one of the facilities) is placed into the neighborhood context, it can affect environmental and social issues. Therefore, the neighborhood facilities and services would not purely increase all residents’ satisfaction, well-being and quality of neighborhood through its equal accessibility for all residents. This paper discusses how the quality of the neighborhood can be enhanced and promoted by the different characteristics of facilities like schools.The research method is based on an analysis of the affective features of a primary school on social sustainability in the neighborhood. The research has been done by recording sample participants’ ideas. The questionnaires were administered to 285 participants from two neighborhoods (with neighborhood-school and non-neighborhood-school) in Kerman, Iran. Results confirm that important features of the neighborhood -school have a direct affect on the quality of the neighborhood. The comfort, safety and harmony were identified as the most important of the six presented factors.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.009
GPT teacher head0.309
Teacher spread0.299 · 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.

Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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