Bibliographic record
Abstract
Designers apply different design elements such as form, colour, texture, light, and movement in the design of products.This interdisciplinary study aims to investigate the application of 'biophilic movement' in the design of interactive wearable objects, given that incorporating both natural inspiration (discipline of biology) and physical movement in the design of products can create a pleasurable experience for the users.In order to investigate how designers might incorporate 'biophilic movement' in the design of products, this research draws from the discipline of biology.The study applies inspiration derived from plant and animal movements, which have positive impacts on human psychology.Furthermore, this study takes a user-centered approach by applying different methods: exploring the people's emotional response to different biophilic movements incorporated in designed wearable objects.Based on these emotional responses, this thesis suggests that biophilic movement can potentially create a pleasurable experience and enhance the interaction between people and wearable objects with biophilic movements .The key findings of this study include: 1) Adding biophilic movement can add interest to biophilic wearable objects by engaging the people who interact with it; 2) Identifying and categorizing biologically inspired movements can help designers in the area of biology-to-design; and 3) Presenting a biophilic semantic differential scale that can be used to understand how people interpret movements in biophilic artifacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".