A sustainable design fiction : green practices
Bibliographic record
Abstract
In this article, we argue that an approach informed by practice theory coupled with design fiction provides useful insights into the role of interaction design with respect to environmental sustainability.We argue that a practice-oriented approach can help interaction designers step away from models of individual behavior and studies of artifacts towards seeing sustainable behaviors as part of multidimensional and interrelated practices and practice elements. We analyze two previously conducted studies. The first study of everyday repair focuses on how people repair their broken objects. The second study of green-DIY examines how green enthusiasts facilitate their practices of making sustainable DIY (do-it-yourself ) projects. We describe the practices of everyday repairers and green enthusiasts in terms of materials, competences, and meanings, and the interrelations among those elements, using the framework of Shove et al. [2012]. We argue that understanding the dynamics of practice and their unique configurations is a starting point to redefine the roles of sustainable interaction design (SID). We propose that designers design towards resources and tools in ways that reflect on the challenges of intelligibility of their design interventions in practices. In addition to considering SID in the light of practice theories, we reveal how design fictions are readily incorporated into green practices in ways that transform those practices and hold implications for transformations of design as well. We bring forward opportunities for designers to co-design with DIY enthusiasts, targeted as practitioners in their own right, designing toward or within a design fiction. As a result, we conclude with the possibility for sustainable interaction designers to become practice-oriented designers who design with transparent open strategies and accessible materials and competences.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".