The unrealized potential of community science to support research on the resilience of protected areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it