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Record W3044054816 · doi:10.1177/1206331220944103

(In)visibilizing Vulnerable Community Members: Processes of Urban Inclusion and Exclusion in Parkdale, Toronto

2020· article· en· W3044054816 on OpenAlexaboutno aff
Elena Ostanel

Bibliographic record

VenueSpace and Culture · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
FundersEuropean Commission
KeywordsInvisibilityPublic spaceInclusion (mineral)SociologyInclusion–exclusion principleSpace (punctuation)DenialAffect (linguistics)Vulnerability (computing)VisibilityDimension (graph theory)Social exclusionCriminologyPolitical scienceGender studiesGeographyComputer securityPoliticsPsychologyComputer scienceEngineeringCommunication

Abstract

fetched live from OpenAlex

The visibility and invisibility of vulnerable individuals or groups in public space have been extensively used as a conceptual tool to assess the “public” character of space. This article analyses the case study of the Parkdale neighborhood in Toronto demonstrating how public space is constructed in a path-dependent territorial process where different layers play a dynamic constitutive role: a material, a discursive, and a policy dimension. It argues that urban visibilization and invisibilization in public spaces extensively affect the dynamics of urban inclusion and exclusion, particularly when they are used in specific territorial stigmatization and destigmatization processes. The investigation enables to better understand the socio-spatial conditions comprising the “denial” and “recognition” of certain groups and individuals at the neighborhood level by understanding how local policies and community-based practices influence the complex dynamic of “seeing and being seen” in an urban environment.

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)0.002
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 score0.093
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.009
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.290
Teacher spread0.268 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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