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Record W2541901577

On the Path to Material Re-Use: Navigating the complexity of material sustainability for architectural practice

2016· dissertation· en· W2541901577 on OpenAlexaboutno aff
Anna Beznogova

Bibliographic record

VenueUWSpace (University of Waterloo) · 2016
Typedissertation
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityArchitectural engineeringPath (computing)Computer scienceConstruction engineeringEngineeringProgramming language
DOInot available

Abstract

fetched live from OpenAlex

The aim of this thesis was to understand how to define sustainability holistically, and how architecture can contribute to holistic sustainability by way of its material form. I conducted a literature review of definitions for sustainable development, looking for a holistic definition that addressed common attitudinal barriers to its practice. It became apparent that it’s useful to study sustainability under a systems science framework that takes environmental, social, and psychological sustainability as interdependent variables. In accordance with this, I reviewed different approaches to material sustainability in architecture, the lifecycles of several common building materials, and the links between material industries, to establish a system-based understanding of how material sustainability can be practiced. In the latter part of my thesis I focus on material re-use as an underrepresented approach to material sustainability, and study the opportunities and barriers in practicing it, particularly in the context of Southern Ontario. I propose that a monitoring tool that draws on public data sources could relieve one barrier to using reclaimed materials by making it easier to find available sources. I find that material re-use can be an architect-driven way to practice material sustainability, it conveys a message about the problems of materialism in our society, and it provides challenging but fulfilling craft-based work.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.638

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 head0.013
GPT teacher head0.226
Teacher spread0.212 · 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
Published2016
Admission routes1
Has abstractyes

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