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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".