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Record W4254853008 · doi:10.32920/ryerson.14652024

Instruments of Gaia

2021· preprint· en· W4254853008 on OpenAlexaff
Matthew Ferguson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAnthropoceneArchitectureExpression (computer science)PoetryExperiential learningEpistemeAestheticsComputer scienceArchitectural engineeringSociologyHistoryEpistemologyArchaeologyEngineeringEnvironmental ethicsArtLiteratureSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

Instruments of Gaia is a thesis which interviews the role of architecture as an interface between nature and human experience. The contained argument is a critique on architecture’s reliance on climatic mitigation; whereas relationships between site and user are nullified through concealed building systems. This thesis project as a counterpoint explores new ideas of architectural design and expression as a register for and active agent in the anthropocene where the magnification and understanding of place and site plays a key role in the development of the climatic imagination needed ever more, to grapple with human impact on the very ground we rest our foundations upon. Architectural expression as an intuitive, didactic mechanism and amplifier of this relationship is explored through the collapsing of multidisciplinary research, and expression of the fragility, and enormity of Gaia as a new secular mythos of the cosmos, and the embedding of these new myths, through weathering and geologic time scales, into an architectural project. The experiential memories formed within this expanded field of architecture constitute a new body of sensible knowledge. The art of architecture offers the medium with which to collapse these assemblages into poetic space and memory, and the beginning of a new dialogue with the Gaia of the Anthropocene.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.989

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

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 head0.046
GPT teacher head0.244
Teacher spread0.198 · 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.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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