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Record W4292092764 · doi:10.1111/ciso.12439

A Heterotopology of Urban Margins: Publicness in the Space of the City

2022· article· en· W4292092764 on OpenAlexafffundabout
Yvonne Wallace, Meg Stalcup

Bibliographic record

VenueCity & Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of OttawaUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHeterotopia (medicine)Urban theoryPublic spaceSociologyUrban spaceRelation (database)SubjectificationOpposition (politics)Political scienceLawRegional science

Abstract

fetched live from OpenAlex

Abstract Urban marginality is generally considered through the lens of exclusion. Policing and public policies, underpinned by private NIMBY (not‐in‐my‐backyard) attitudes and actions, aim to remove marginalized individuals from urban spaces. Yet they remain and are visible. As such, individuals across the urban margins share in publicness, which is the connection between strangers unique to cities borne from their visibility to one another. We suggest that publicness provides the terrain for an impactful mode of urban study, one which attends to the transitory encounters that anthropology has traditionally tried to transform and thicken. The ways in which individuals perform when they are visible in public displays something of how they understand themselves in relation to others, a form of mutual subjectification in which we as researchers are also implicated. Drawing on fieldwork in Ottawa, Ontario, with individuals who use drugs and individuals who panhandle, we analyze urban margins as other spaces and publicness as other relations using Foucault’s concept of heterotopia. This resulting heterotopology shows a different version of marginality in the city in which urban margins are not separate from but connected to the urban experience.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.317
Teacher spread0.279 · 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

Citations4
Published2022
Admission routes3
Has abstractyes

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