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Record W2585303611 · doi:10.36939/cjur/vol25no2/art45

From Slum to Village: A Semiotic Analysis in Reimaging Urban Space

2016· article· en· W2585303611 on OpenAlexaffvenueabout
Harry H. Hiller, Pernille Goodbrand

Bibliographic record

VenueCanadian journal of urban research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSemioticsReinterpretationDowntownRedevelopmentSlumSociologySpace (punctuation)PoliticsClass (philosophy)Relation (database)PopulationGeographyAestheticsPolitical scienceEpistemologyLinguisticsArtDemographyLawArchaeologyComputer science

Abstract

fetched live from OpenAlex

The transformation of inner city spaces has been dominated by explanations stressing political economy factors such as rent gap and cultural factors such as urban amenities. This paper takes a different approach in that it uses the tools of urban semiotics to show how the representations of space in a downtown location of protracted decline in the Canadian city of Calgary are transformed discursively and experientially to produce a different image for a different social class. What made this reimaging of space so critical was the fact that the displacement of the existing population was rejected which called for a powerful and aggressive semiotic reinterpretation of the area. The semiotic strategies are discussed in relation to the material changes which reveal the contradictions and dilemmas in attempting to create a mixed class community through revitalizing imagery rather than merely redevelopment.

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)0.002
metaresearch head score (Gemma)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.045
Scholarly communication0.0090.004
Open science0.0010.006
Research integrity0.0010.002
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 head0.057
GPT teacher head0.382
Teacher spread0.325 · 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
Published2016
Admission routes3
Has abstractyes

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