What Do Stakeholders Value? Integrating Value Sensitive Design Techniques into an Impacts-Focused Design Course
Bibliographic record
Abstract
Value sensitive design is a relatively new design process that emphasizes the inclusion of socio-technical considerations. In this work, value sensitive design techniques were integrated into a second-year design course. Five techniques were incorporated: value identification, stakeholder mapping with a value source analysis, value scenarios, ethnography, and multi-lifespan timelines. A beginning and end of term survey was administered to understand students’ value perceptions. This survey showed a significant (p = 0.00124) increase in students’ self-perceived ability to integrate values in the design process. Through course deliverables, students showed an ability to make connections between values, technologies, and stakeholders, including value alignments and tensions. However, students struggled to find supportive evidence and explain the potential impact of values on the design process. Some techniques like holistic stakeholder identification and value source analysis were effectively integrated into the course while others, like value scenarios and ethnography, were less successful.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".