Reconceptualizing conservation
Bibliographic record
Abstract
Early definitions of conservation focused largely on the end goals of protection or restoration of nature, and the various disciplinary domains that contribute to these ends. Conservation science and practice has evolved beyond being focused on just issues of scarcity and biodiversity decline. To better recognize the inherent links between human behaviour and conservation, “success” in conservation is now being defined in terms that include human rights and needs. We also know that who engages in conservation, and how, dictates the likelihood that conservation science will be embraced and applied to yield conservation gains. Here we present ideas for reconceptualizing conservation. We emphasize the HOW in an attempt to reorient and repurpose the term in ways that better reflect what contemporary conservation is or might aspire to be. To do so, we developed an acrostic using the letters in the term “CONSERVATION” with each serving as an adjective where C = co-produced, O = open, N = nimble, S = solutions-oriented, E = empowering, R = relational, V = values-based, A = actionable, T = transdisciplinary, I = inclusive, O = optimistic, and N = nurturing. For each adjective, we briefly describe our reasoning for its selection and describe how it contributes to our vision of conservation. By reconceptualizing conservation we have the potential to center how we do conservation in ways that are more likely to result in outcomes that benefit biodiversity while also being just, equitable, inclusive, and respectful of diverse rights holders, knowledge holders, and other actors. We hope that this acrostic will be widely adopted in training to help the next generation of conservation researchers and practitioners keep in mind what it will take to make their contributions effective and salient.
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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 | 0.044 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.012 | 0.111 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.008 | 0.032 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".