Elegant conservation: reimagining protected area stewardship in the 21st century
Bibliographic record
Abstract
We present an approach to the conservation of protected areas that aligns cultural truths with scientific truths to increase community capacity for conservation. This alignment, which we call elegant conservation, asks protected area managers to reimagine how conservation can be inclusive of cultures and subcultures whose members value protected areas, but not in the same way. Reimagining how protected area managers approach conservation requires them to observe closely and holistically, with fresh eyes, human consciousness and behavior as well as relationships between people and between people and nature. Our approach connects the humanities, Western sciences, and other forms of knowledge, including Indigenous knowledge, in a manner that more sustainably builds social support for conservation. We first offer a heuristic of seven conditions that protected area managers can analyze when sizing up conservation issues and the people involved. We then propose a heuristic of five human tendencies—elements of human consciousness—that can help protected area managers and their partners organize constructive responses to any conservation issue. Our model of elegant conservation offers a pragmatic, holistic, inclusive alternative to top-down, reductive management approaches and is an outgrowth of modern American intellectual history, especially since the end of the Cold War, ca. 1989–1990. Elegant conservation presents an opportunity to help people find common ground and move protected area management beyond its origins in settler colonialism at a time of national and planetary crisis.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".