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Record W599476222

Re-Visioning Sustainable Urban Housing in2020, the year of perfect vision

2009· dissertation· en· W599476222 on OpenAlexfundaboutno aff
James Arvai

Bibliographic record

VenueUWSpace (University of Waterloo) · 2009
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsEnvironmental planningGeography
DOInot available

Abstract

fetched live from OpenAlex

Civilization’s vantage point has shifted with advances in technology from an eye-level view of the horizon to a bird’s eye view from a plane, to a planet-wide view from space. This relatively new global view is now the cultural perspective and embraces the holistic view of the biosphere as a large, interconnected, complex habitat that is subject to ever increasing anthropogenic pressures. The newly realized global perspective and realizations of global scale man-made impacts has added the concept of sustainability to the architectural realm. Architectural design issues of sustainability are inherently multi-scale, interconnected, and complex; and can not be resolved with western reductionist science alone. The holistic perspective is a core component of the evolving analysis methodology for pursuing insights on the interactions and connectivity of sustainable design. This thesis will speculate on the future of sustainable urban housing as a nonlinear outcome resulting from the rebalance of culture, technology and economy interacting with choice in our society. Through time, the interactions of these changing major forces is converging on a new equilibrium point that, to some extent, can be moved by choice. The architecture of urban housing has a potential role to play in moving that rebalance point in the future towards sustainability. This thesis will attempt to put on stage a context for urban housing in Canadian society that is transitioning towards sustainability in 2020, the year of perfect vision.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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 designQualitative
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
Published2009
Admission routes2
Has abstractyes

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