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Record W2975745963 · doi:10.1162/desi_a_00564

Unequal Ideas: Reflections on Designing Politics, an Urban Ideas Competition in Rio de Janeiro

2019· article· en· W2975745963 on OpenAlexfundno aff
Adam Kaasa

Bibliographic record

VenueDesign Issues · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
FundersNational Endowment for Science Technology and the ArtsYork UniversityLondon School of Economics and Political ScienceAndrew W. Mellon Foundation
KeywordsCompetition (biology)ScholarshipExhibitionPoliticsPower (physics)InequalityColonialismGlobal SouthJurySociologySocial inequalityPolitical sciencePolitical economySocial scienceEconomic geographyGeographyLaw

Abstract

fetched live from OpenAlex

This article initiates a discussion about the unequal geography of the labor that challenges institutions and processes of public scholarship in design. The comparison between the urban competitions in New York, London, and Rio de Janeiro demonstrates that it was only in the Global South that challenges to the technology of the competition were raised. These challenges were based on issues of power imbalances between institutions both within and between the Global North and Global South, and around questions of the social inequalities embedded in the structures of the competition itself (the submissions, the jury, the exhibition). Through this analysis, the article suggests that the burden of labor for decolonizing rests on those already oppressed by systems embedded in the continuous presence of colonialism.

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)0.011
metaresearch head score (Gemma)0.012
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: none
Teacher disagreement score0.031
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0270.061
Scholarly communication0.0150.006
Open science0.0020.012
Research integrity0.0040.004
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 head0.113
GPT teacher head0.375
Teacher spread0.262 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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