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Record W4390737601 · doi:10.35483/acsa.am.110.53

Eurocentric Legacies: The Institute for Architecture and Urban Studies and Delaying Change in Architecture in 1970s New York City

2022· article· en· W4390737601 on OpenAlexaboutno aff
Marcelo López-Dinardi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureSociologyReading (process)Plan (archaeology)ExhibitionPoliticsMedia studiesHistoryVisual artsArt historyPolitical scienceLawArt

Abstract

fetched live from OpenAlex

This paper examines how the Institute for Architecture and Urban Studies (IAUS) that existed in New York City between 1967-1984, constructed a space that reinstated a Western epistemology for architecture and created an audience and discourse for an emerging architecture scene in a distressed New York through its events and media during the 1970s. Given their resonance, the paper positions current demands for change in architecture education and the profession concerning their equivalent in the late 1960s when the IAUS was founded. This paper will ask whether a change in architecture and non-Eurocentric educational models following the 1960s struggles and upheavals were delayed with the appearance and success of the IAUS in New York City. The paper argues, through a critical reading of their media apparatus (exhibitions, lectures, classes, journals, and books), notably the ambitious OPEN PLAN series, and their undeniable success, that the IAUS’s reinstated a Eurocentric legacy—delaying change, the reckoning of architecture’s role in racial, social, and political asymmetries, and advanced architectural disciplinary ideas’ marketization in an emerging neoliberal rationale. Finally, this paper discusses existing scholarly work around the IAUS and first-hand research from the IAUS’s collection archived at the Canadian Centre for Architecture in Montreal.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

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

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 head0.065
GPT teacher head0.258
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

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