Experiences in Transdisciplinary Education for the Sustainable Development of the Built Environment, the ISAlab Workshop
Bibliographic record
Abstract
There is a growing recognition and acceptance that society needs to develop new pathways to achieve a more sustainable future. Our current model of development poses significant challenges when it comes to achieving a more just society based on respect for nature and human rights, and demands a sustainable economy supported by a new circular model supporting the UN sustainable development goals. Higher Education Institutions (HEIs) accordingly have developed Master programs that are responsible for providing fundamental services in the joint effort towards sustainability. Meanwhile, leading Universities around the world have developed other very relevant programs. The open and unstructured challenge of sustainability poses an obstacle to existing academic structures. Specifically, the built environment is one of the leading contributors to challenges addressed in the programs such as: Anthropogenic climate change, resource depletion, waste generation and pollution, poverty, and inequity. The Interdisciplinary Sustainable Architecture lab (ISAlab) explores these issues as part of a multidisciplinary approach involving the collaboration of leading Universities from different areas on the world through an innovative initiative. This paper explores the experiences of the ISAlab workshop, which has been taking place yearly in Valencia since 2017. The workshop draws together students from a range of disciplines from across engineering and science, law and the social sciences and from a range of countries and backgrounds, including North and South America, Europe, and Asia. Its purpose is to facilitate a rich co-creative learning environment led by (engineering) academic faculties from across Europe (Spain, the UK, France, Germany, Netherlands and Ireland) as well as North America (the US and Canada), as well as local experts who helped provide participants with appropriate context and guidance. The objective is educating future engineers that are capable of finding robust ways to implement sustainability at a practical level on the built environment, taking account of the multidisciplinary perspective and with the incentive of solving real-life problems.
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".