Bibliographic record
Abstract
The following chapter by Eric Benson and Priscilla Ferronato discusses how teaching design through the process of systems thinking, as derived from the disciplines of both ecology and biology, is the best path forward to prevent the worst-case scenarios of climate change. Systems thinking is a process that can help designers to uncover the root cause of a problem and how it connects to the larger picture: people, profit and planet (and everything in between). The conditions of the Anthropocene mean that designers must be able to identify the social, political and environmental repercussions of their work – and take responsibility for them. This process empowers designers to evaluate and shift the emphasis of their outcomes to consider the demand put on our natural resources: where and how we get materials to produce our projects, who and what is affected by our decisions and what will happen to the project after it is implemented. The systems thinking process explored in this chapter is a four-step model (determine ¬project goals, map out the design problem, brainstorm design outcomes and evaluate each possible design outcome) as set forth in the 2017 book Design to Renourish: Sustainable Graphic Design in Practice. The authors, who are based at the University of Illinois at Urbana-Champaign, taught this systems thinking model over two years in three different courses to test its effectiveness and make improvements to the process, methods, tools and resources from one academic term to the next.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".