The de mestral project: Using macro photo-journaling to stimulate interest in bio-inspired design and science, technology, engineering and mathematics disciplines
Bibliographic record
Abstract
Bio-inspired design (BID) and its many variants (biomimetics, biomimicry etc.) continues to be a promising innovation methodology in which practitioners from industry and academia search nature's evolutionary diversity for meaningful design opportunities.However, despite BID's potential to contribute greater value to society, it remains an obscure field.In this paper, we present a case demonstrating how a novel educational exercise could play an important role in advancing the field by stimulating student interest in BID and the more broadly associated Science Technology, Engineering and Mathematics (STEM) fields which drive it.Specifically, we discuss a cross-disciplinary university seminar that uses an experimental photo journaling exercise called the "de Mestral Project" which aims to recreate the successful invention process of Velcro by Engineer Georges de Mestral.This exercise cultivates observation skills, or the ability to look closer at the natural world as a foundation for uncovering new opportunities for design.Critical to the activity is the use of macro photography to help facilitate this discovery process.The outcome of this project has led to early stage BID concepts that have been explored more deeply in subsequent funded research efforts and in capstone Industrial Design studio projects.The development of the de Mestral project in ongoing; the purpose of this paper is to describe its methods and preliminary outcomes.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".