Sustainable Futures: Learning from Copenhagen
Bibliographic record
Abstract
This article analyses and explores the implications of the “Copenhagen Lessons – 10 Principles to Build On”, presented at the conclusion of the 28th UIA World Congress of Architects, “Sustainable Futures – Leave No One Behind” (held in Copenhagen, 2023). The principles address architecture’s contribution to the UN Sustainable Development Goals (SDGs) and outline targets for the new practice needed to meet the goals. The article discusses how the principles can be localized and implemented to enable further, decisive action in the building industry, in educational institutions, among government officials and in society at large. The article is a contribution to a new language and framework of how to understand architecture and points to some of the many new architectural solutions, responding to local climate, culture, and challenges. The change needed to address social needs while protecting the planet is mayor, and the collective challenge demands a new way of talking about architecture and, crucially, new ways to design, plan and build as we go forward.
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 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".