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Record W4384695723 · doi:10.22215/etd/2023-15553

Land-Scape/Tree-Scape/Slow-Scape

2023· dissertation· en· W4384695723 on OpenAlexaboutno aff
Connor Nicholas Tamborro

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsScapeMindsetArchitectureMeaning (existential)Resistance (ecology)SlownessAestheticsBuilt environmentSociologyVisual artsPsychologyEngineeringComputer scienceArtEcologyCivil engineeringArtificial intelligenceGeology

Abstract

fetched live from OpenAlex

As the world becomes led by fast-paced practices, critical engagements with physical presence of the environments are decreasing. These superficial engagements made through the ‘digital’ is eroding a critical awareness of presence, even though human conditions have had a history with space whereby people are personally, physically, and mindfully immersed. As a result of this new digital mindset and 'rushed' modes of engagement, there has been a shift in human awareness: Spatial architecture is perceived differently, and barriers have been created in fostering connections. In response, this thesis seeks to explore the timeless authenticity of a place through the meaning of 'slowness' in architecture. Using a site in Combermere, Ontario, a process of critical site engagement was used to re-discover the meaning of ‘slowness’ in architecture. The result is a resistance that positions deep and meaningful engagements as an antidote to the ‘fast,’ blurry, and constructed experiences offered through technology.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.011
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.008
GPT teacher head0.215
Teacher spread0.208 · 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 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
Published2023
Admission routes1
Has abstractyes

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