Sustainable Creativity: Overcoming the Challenge of Scale When Repurposing Wind-Turbine Blades
Bibliographic record
Abstract
Abstract With the growing adoption of wind-energy technology to help address climate change, we must now also consider the disposition of retired wind-turbine blades, which are not easily recycled. This pressing environmental problem was used as the prompt in a creativity study, where participants were asked to identify potential reuses in a wind-turbine-blade repurposing task (WRT). In past iterations of this study, participants consistently struggled with correctly incorporating the large physical size of wind-turbine blades in their reuse concepts. The Alternate Uses Task (AUT) is an established measure of creativity that involves asking participants to identify uses for common objects like bricks and paper clips. The current work explored whether an AUT can be adapted as an intervention to help overcome the WRT scale challenge so that the appropriateness of reuse concepts can be improved. Students in a fourth-year undergraduate engineering-design course (N = 28) underwent both of two conditions, a scaled-AUT intervention and a typical-AUT control, before the WRT. The results support that a main difficulty with the WRT is object size. Both fluency and flexibility (number and categories of ideas) for the relatively common AUT objects were significantly lower in the scaled AUT than in the typical AUT. However, correctly scaled WRT concepts significantly increased after the scaled AUT, supporting the intervention's effectiveness. While motivated by the real-world problem that decommissioned wind-turbine blades present, the current work focuses on conceptual design and creativity, where incorporating real-world problems may provide value beyond more typical AUTs, which have fewer real-world applications. Thus, for future work, the WRT is proposed as a standard design-study task whose solutions help address a real-world problem.
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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".