Affordances and Product Design to Support Environmentally Conscious Behavior
Bibliographic record
Abstract
We developed an affordance-based methodology to support environmentally conscious behavior (ECB) that conserves resources such as materials, energy, etc. While studying concepts that aim to support ECB, we noted that characteristics of products that enable ECB tend to be more accurately described as affordances than functions. Therefore, we became interested in affordances, and specifically how affordances can be used to design products that support ECB. Affordances have been described as possible ways of interacting with products, or context-dependent relations between artifacts and users. Other researchers have explored affordances in lieu of functions as a basis for design, and developed detailed deductive methods of discovering affordances in products. We abstracted desired affordances from patterns and principles we observed to support ECB, and generated concepts based on those affordances. As a possible shortcut to identifying and implementing relevant affordances, we introduced the affordance-transfer method. This method involves altering a product's affordances to add desired features from related products. Promising sources of affordances include lead-user and other products that support resource conservation. We performed initial validation of the affordance-transfer method and observed that it can improve the usefulness of the concepts that novice designers generate to support ECB.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".