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Record W4384697701 · doi:10.22215/etd/2023-15494

Dirt: Making with Contaminated Lands

2023· dissertation· en· W4384697701 on OpenAlexaboutno aff
Ju Huang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsDirtAnthropoceneFeelingAestheticsOrder (exchange)ArchitectureEnvironmental ethicsHistorySociologyGeographyArchaeologyEpistemologyArtCartographyPhilosophyBusiness

Abstract

fetched live from OpenAlex

My thesis explores dirt in the contemporary world and its place in architecture through a series of ceramic-making exercises. Using my hands, my eyes, and my mind in these exercises, I make sense of the multiple meanings and the critical matter of dirt through feeling and thinking. My sense of touch helps me explore the place and non-place of clay, my eyes aid me in clarifying the scientific order in modernity that turns dirt into waste, and my mind eventually brings me to tackle the critical problem of discarded waste sites called brownfields in Canada. The series of experiments has created a new sensory order from which a tectonic expression of architecture rooted in a particular place is created – a process that trespasses the taboos of the contaminated and embraces the uncertainties contained in earth and land in the technological age 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.461

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)0.0000.000

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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
Published2023
Admission routes1
Has abstractyes

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