Sustainable Design Issues in Sincerity Expression: With the Case of Gift-Wrapping
Bibliographic record
Abstract
People often sacrifice themselves for sustainability, but they do not often consider sustainability when serving others to express sincerity. In this study we focus on the activity of gift-wrapping1, as an important social behavior that creates notable waste. The purpose of this research is to investigate the general approach to gift-giving and gift-wrapping regarding sustainability, and how it might be possible to embody sustainability without degrading sincerity. A focus group and generative workshop were conducted respectively, and insights from the focus group were used when setting up the generative workshop. Through this study, we found that emotional value cannot be compromised by sustainability when expressing sincerity in gift- wrapping. Gift givers do not want to forfeit any sincerity by using eco-friendly materials that could be seen as inappropriate to the recipient. However, from the generative workshop, we found that people can show their true intentions by incorporating messages, by using conservable products that are reusable in other situations, by adding visual aesthetics, and by adding surprise aspects. This result shows that we can creatively adapt sustainability while keeping our emotional values. 1 In this study, we differentiate the term 'gift-wrapping' from 'packaging'. It focuses on the additional decoration which can be even wrapped over the packages.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| 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".