EXPLORE, RESPOND, ADAPT: THE ROLE OF RISK AND EXPERTISE IN HYBRID (SOFT/HARD) PRODUCT EDUCATION
Bibliographic record
Abstract
Design education is working in an expanding field of environmental contexts.The coming generations will witness changing climates that drive them toward migration to the poles and survivalism.As we explore and settle further from our familiar locales, how might we respond to risk before we adapt?Why are risk and expertise in extreme environments relevant to future design education?In accessing extreme environments, humans require technical products and protective equipment (PPE) to survive and thrive.This paper shares experience from hybrid (soft and hard) product design education where extremes (environment and context) inform the curriculum.Projects are challenge-based and set in high-risk environments.Starting from the unfamiliar environmental contexts, students learn from experts who have mitigated risks and developed specialty knowledge-base and technical skills relevant to this expanded field.The year 3 curriculum model is collaborative, explorative and technically demanding with a 7-week project involving expertise in technology, the body and context/users.Iterative prototyping happens in on-site speciality labs with early and frequent testing.The student teams self-organize and project-manage their way to a full scale, functional prototype that is evaluated through design scenarios, expert feedback, field-based test protocols (on and off-site).This paper reflects on project outcomes over 5 years.Informed by student and stakeholder feedback, it offers perspectives and recommendations on the necessity of an expanded 'environmental' field for this generation of risk-engaged designers.Future-proofing design education derives benefit from introducing the unfamiliar and unknowns so students can explore, respond, and adapt as designers.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".