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Record W7137581062

Spatialized Injustice in the Contemporary City

2022· other· en· W7137581062 on OpenAlexfundno aff
Sarah Elizabeth Barrett

Bibliographic record

VenueOAPEN (The OAPEN Foundation) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInjusticeCONTESTEveryday lifePublic spaceRight to the citySocial injusticeEconomic JusticeSpace (punctuation)
DOInot available

Abstract

fetched live from OpenAlex

This volume documents research illustrating public dissents and interventions to injustice in modern-day cities. Authors present everyday occurrences of city life and place making; still, they show how the ordinary city grows from historical dimensions of injustice, violence and fear. Yet, ordinary citizens continue to make the city their own, to contribute to the creation of city structures and to contest those practices of spatial demarcation, which limit rather than uplift their everyday social livelihood. Chapters show how marginalized populations, from racial, to gendered, to the working poor, are part of the apparatus that makes the city function. However, their contributions to city arrangement and endurance are perpetually at the margins, and city spaces continue to be designed in ways that ignore and negate the existence of those who protest inequity. Novel to the volume are chapters that document and illustrate contestations of city spaces through artistic representation. Public spaces like schools, art galleries and museums are presented as central to projects of inhabiting, remembering and reimagining (in) the just city. Still, ordinary city spaces, like the public washroom, illustrate issues of gender inequity, spatial bias and other art-based protests. City dwellers interested in learning about ‘the making’ of the city; and those interested in the city as a space of possibilities – and the good life, will benefit from this volume. Scholars of geography, space, art and social justice will marvel and simultaneously be appalled by the everyday minute, yet shocking descriptions of the complexity – and unfairly structured city spaces in which they dwell.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0160.052
Scholarly communication0.0150.005
Open science0.0010.014
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.054
GPT teacher head0.308
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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