On the Path to Material Re-Use: Navigating the complexity of material sustainability for architectural practice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.076 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".