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Record W3209730982 · doi:10.32920/ryerson.14643849.v1

Nothingness: an exploration of dissolving architecture

2021· preprint· en· W3209730982 on OpenAlexaff
Jessica Stanford

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsToronto Metropolitan University
FundersDivision of Graduate Education
KeywordsNothingArchitectureExistentialismAestheticsEpistemologyObjectificationSociologyComputer sciencePhilosophyVisual artsArt

Abstract

fetched live from OpenAlex

This thesis explores the idea of nothingness from a variety of perspectives in order to better understand how this notion might manifest in architecture. Taking the critiques of objectification and architectural worthlessness by Dejan Sudjic and James Howard Kunstler as points of departure, the research involved an examination of spiritual and philosophical traditions dealing with nothingness, including the traditional ideas of Buddhism and the phenomenological and existential perspectives that developed in the twentieth century. Research into artistic and architectural manifestations of these perspectives provided important examples of how the abstract idea of nothingness could be translated from a purely analytical to a projective practice. Through a series of experiments on nothingness and space, a technique was developed to produce architecture in a thoughtful and meaningful manner rather than to produce the architectural garbage, the unconscious architecture, the visible entropy that Kuntsler and others refer to. Ultimately, however, nothingness can act as an architectural device that distills an idea – it is the pause in the chaotic life of consumption, the still point in a turning world.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.042
Scholarly communication0.0070.011
Open science0.0010.009
Research integrity0.0020.003
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 head0.094
GPT teacher head0.285
Teacher spread0.191 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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