Designer Ecosystems: Incorporating Design Approaches into Applied Ecology
Bibliographic record
Abstract
To satisfy a growing population, much of Earth's surface has been designed to suit humanity's needs. Although these ecosystem designs have improved human welfare, they have also produced significant negative environmental impacts, which applied ecology as a field has attempted to address and solve. Many of the failures in applied ecology to achieve this goal of reducing negative environmental impacts are design failures, not failures in the science. Here, we review (a) how humans have designed much of Earth's surface, (b) the history of design ideas in ecology and the philosophical and practical critiques of these ideas, (c) design as a conceptual process, (d) how changing approaches and goals in subfields of applied ecology reflect changes and failures in design, and (e) why it is important not only for ecologists to encourage design fields to incorporate ecology into their practice but also for design to be more thoroughly incorporated into ours.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".