The unrealized potential of community science to support research on the resilience of protected areas
Bibliographic record
Abstract
Abstract To remain effective into the future, protected areas must be resilient to change. Evaluating the resilience of protected areas requires data across large spatial and temporal scales, which has proven to be a strength of community science in conservation research. Here, we assess the contributions of community science to different topics of protected area research and identify gaps where community science can be used more effectively. We performed a literature search aimed at capturing the research on resilient protected area design and management, then used Latent Dirichlet Allocation to model the topics represented in this corpus. Once topics were established, we searched for evidence of community science being used in each publication. Our analysis showed that there are five main areas of focus in resilient protected area research: biodiversity, climate change, connectivity, resources and ecosystem services, and social governance. We found limited evidence in the literature of community science directly assisting research in these areas. Community science has proven effective for extensive and cost‐effective data collection in other situations; therefore, we recommend ways in which conservation managers and researchers can incorporate community science in the design and management of resilient protected areas.
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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 | 0.244 | 0.416 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".