Biologically informed disciplines: a comparative analysis of bionics, biomimetics, biomimicry, and bio-inspiration among others
Bibliographic record
Abstract
This article offers a complementary approach to research and education in biologically informed disciplines through the lens of bionics, biomimetics, and biomimicry terminology. For the purpose of developing this approach, we look at past and current contexts in which the three fi elds have emerged and identify three issues: an absence of common ground that unites the fi elds of bionics, biomimetics, and biomimicry while recognizing their contextual differences, a non-standardized use of the terminology that leads to ambiguity within the fi eld of biologically informed disciplines, an incomplete and disorganized historical and contextual knowledge about the fi eld that inhibits a common starting ground for collaboration, and confuses non-scientists who seek biological understanding. We offer a fundamental understanding of the fi elds from theoretical perspective by bringing together opinions of researchers and practitioners of bionics, biomimetics, biomimicry, bio-inspiration and offering a comprehensive analysis of terms culminating in the introduction of an overarching term 'biologically informed disciplines'.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".