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Large City Social and Spatial Segregation in Youth’s Opinion

2018· article· en· W2907698052 on OpenAlexaboutno aff
Natalya L. Antonova, Elena Grunt, Anatoly Merenkov

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
FundersUral Federal University
KeywordsResidenceGeographyHuman settlementSpace (punctuation)Quarter (Canadian coin)Regional scienceCivilizationSocioeconomicsSociologyDemographyArchaeologyComputer science

Abstract

fetched live from OpenAlex

The article deals with the problem of the socio-spatial segregation of the city as the isolation of social groups in the urban space. As human civilization advances, cities are transformed into "dominant types of settlements", which makes it important to study the phenomena of urban life, including urban social segregation. The major research objective is to analyze the youth' attitudes toward the city as a space of vital activity and to assess the city space segregation of Yekaterinburg. The research methodology combines qualitative and quantitative methods. Methods of collecting primary data were the questionnaire and in-depth interview. 200 young people were questioned at the age of 18 years and older on the basis of the "a sampling in 2015 in the city of Yekaterinburg. Depths interviews with experts (n=15) are applied on purpose to identify main problems of the topic. The investigation has revealed that the city of Yekaterinburg in Russian youth' opinion is divided into specific local units: the core and the periphery. At the same time, the periphery is divided into two large zones: industrial quarter and commune of residence which are located in different parts of the urban space. Two thirds of the respondents are satisfied with their area of residence and 30% of the respondents would like to move to the center of the city. The research has fixed the key issues which exist in the city periphery. The majority of uncontrolled parking lots near residential buildings and transport lines congestion are the main issues of commune of residence. Insufficient territory's cleaning, poor streets lighting and the need for green spaces are the key issues of industrial quarters. The survey has shown that the urban space is divided into three zones: the core (center) with modern buildings of executive housing in which high-income people live, the periphery commune of residence where the middle class lives, and industrial quarters of the periphery where people with lower incomes live. © Published under licence by IOP Publishing Ltd.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.038
GPT teacher head0.273
Teacher spread0.235 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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