Bibliographic record
Abstract
Unprecedented extreme climate emergencies are becoming part of everyday conversations and experiences. As students seek how to design for these challenges, design educators need to enhance learning in the area of *habitability in extreme environments. Author Solnit defines emergency as “separation from the familiar, a sudden emergence into a new atmosphere” (p.x, 2009). The authors’ experience in extremes and habitability* inform design education projects for unfamiliar, remote settings, where the challenge is inaccessibility to real end users and real-time conditions. This is a case study of a habitat designed and prototyped by a student team in one location, installed and inhabited in a remote setting by analogue (where one situation is intended to simulate another: a common approach in space architecture) astronauts. (*design of suitable living conditions/life support systems). Project Design briefs invite students to frame a problem to generate and test prototypes to an expected final state, leading them to develop skills, confidence and competencies. For the case study described, the full-scale prototype was installed in a remote lava tube (representing a subsurface cave on the moon) and used by two crews during two missions. Extreme contexts can captivate students and lead to spectacular concepts. While this project’s success was the development of a prototype, the habitability experience was problematic. Post-mission reporting cites the success of a design but not its crew's experience, therefore the authors offer recommendations for extended learning to future-enable design education through field-relevant skills, socially meaningful competencies and resilient contextual solutions.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".