Sustainability in architectural design projects – a semiotic understanding
Bibliographic record
Abstract
The potential of semiotics to theorize and analyze the field of sustainable architecture is still largely unexplored. This paper uses a triadic structure for defining sustainable design signs and distinguishes two separate modes of sustainable design reasoning: namely deductive and abductive sustainable design reasoning. This theoretical framework is used to analyze two architectural projects submitted for an international design competition in Montreal, Canada. The architectural texts, considered in this paper the representamen of the signs, prove to be indicative of the mode of reasoning deployed. The analysis also reveals that the mode of reasoning used dictates the types of signs produced, the role designed-objects have in the signs, as well as the functional possibilities design elements perform in the project. The paper proposes that deductive sustainable design reasoning brings to a halt the process of semiosis – presenting a status-quo approach – and that abductive sustainable design reasoning allows semiosis ad infinitum – presenting a future driven outlook. Additionally, a gap appeared between the open form of critical judgement proposed for competitions and the conceptual fixation inherit in deductive sustainable design reasoning. This paper presents a theoretical contribution that provides new possibilities for researchers to model and analyze sustainability in design projects.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".