Design for Wellbeing and Happiness
Bibliographic record
Abstract
The influence of design on wellbeing and happiness is a subject of growing research across various design domains, including products, services, systems, and environments. However, a challenge remains in grounding research on solid theories and methods that can unveil how design impacts people's wellbeing, enabling evidence-based approaches to design. This theme track focuses on contributions from design in fulfilling the societal need to promote wellbeing and happiness, aligned with the conference theme: Resistance, Recovery, Reflection, Reimagination. The conference encourages us to expand our design horizons by reflecting on how the world is challenging the prevailing focus of design, which is often limited to addressing superficial and incremental improvements to existing realities. It urges us to advance our methods, approaches, and processes to effectively solve complex problems. We welcome papers that report on theoretical and empirical studies contributing to developing the 'design for wellbeing and happiness' (DfW) field, addressing individual and/or social challenges. Examples include, but are not limited to: Design and research methods: Reflecting on the challenges and proposing ways to embrace individuals and their subjective experiences in exploring wellbeing and happiness. Design decision-making: Exploring methods, tools, and approaches or research projects that focus on supporting and facilitating decision-making regarding DfW. Evidence-based design: Exploring projects in DfW across various design domains. Emerging technologies: Examining how technology can enhance or negatively impact wellbeing and happiness. Wellbeing and sustainability: Reflecting on the challenges between individual and general goals and needs. Ethics of DfW: Identifying and addressing ethical questions in DfW.
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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".