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Spatial and Social Boundaries in Gentrified Neighbourhoods

2014· article· en· W2091005366 on OpenAlexaboutno aff
Jana Zdráhalová

Bibliographic record

VenueAdvanced engineering forum · 2014
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSpace syntaxGentrificationGeographyCharacter (mathematics)Economic geographyQuarter (Canadian coin)Scale (ratio)Space (punctuation)PopulationUrban spaceArchitectural engineeringArchitectureCzechRegional scienceCivil engineeringSociologyCartographyArchaeologyEngineeringComputer scienceLinguisticsDemography

Abstract

fetched live from OpenAlex

In the paper we analyse the character of spatial boundaries of buildings and examine their correlation with social changes. For a case study we use Holesovice, a quarter of Prague, Czech Republic. This city part is a typical example of originally industrial suburb with a large number of factories, docks and railway station, all built mainly in 19. century. In the last 15 years the area has gone through gentrification that significantly changed its urban and architectural face. The transformation also affected the character of services and cultural facilities available in the area. We compare boundaries of the original construction and boundaries of the new developments built in the last 15 years. The analysis is done at the scale of buildings. Our methodological framework is based on the Space Syntax theory and Urban Morphology. The studied characteristics are integration of accessible spaces and design of boundaries. The paper identifies urban and architectural features that correspond and reflect the lifestyle of the gentrified part of population.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.010
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.173
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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