Exploring the feasibility of Cradle to Cradle (product) design: perspective from New Zealand Scientists
Bibliographic record
Abstract
This research project explores the feasibility of a Cradle to Cradle approach to sustainable product design in New Zealand. Relatively recently a framework for sustainable design was proposed by environmental chemist Michael Braungart and architect William McDonough who suggest that the current paradigm of “cradle to grave” product development is unable to provide a solution to the world’s current ecological crisis, and a “cradle to cradle” framework is more appropriate. They suggest that their approach, based on examples from nature, ensures that all human activities have a positive ecological footprint, capable of replenishing and regenerating natural systems, as well as guaranteeing that we are able to develop a world that is culturally and ecologically diverse. A sample of New Zealand scientists were asked to explore the underlining science and feasibility of the Cradle to Cradle design framework in an attempt to determine the potential of this approach to the design of sustainable products. Analysis of interview data indicated that sustainability is a complex and multi-faceted concept, especially with regard to practical application of ecological considerations, and product design. There is considerable environmental and economic potential in the successful application of cross-discipline collaboration between science and design in addressing the need for products that contribute to sustainable solutions.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".