Identifying synergies and differences in multistakeholder conservation priorities using participatory mapping interviews
Bibliographic record
Abstract
Broad-scale deforestation has reduced habitat area and connectivity for wildlife in the Neotropics. Costa Rica has attempted to reverse this trend by developing a network of biological corridors to connect protected areas. However, individual corridors consist of diversely managed, privately owned land, and corridor councils lack the authority to implement management policies. Thus, locally operating organizations play critical roles in executing conservation initiatives, but little research has examined whether their actions support corridor objectives. To explore how organizations’ conservation priorities align with corridor goals, we conducted semi-structured interviews about land management practices, conservation initiatives, and conservation constraints with key informants from 20 organizations operating within the upper Guacimal watershed, located within the Corredor Biológico Pájaro Campana (CBPC) in northwestern Costa Rica. The interviews included a participatory mapping activity in which participants identified up to five conservation priorities by drawing polygons on a map. Analysis of locations where significant numbers of participant-identified polygons overlapped (hotspots) showed that organizations’ conservation priorities aligned with the primary goal of the CBPC, which is to enhance downslope forest connectivity. Hotspots occurred in middle-elevation areas of moderate to low forest cover that are downslope adjacent to the well-protected high-elevation zone within the study area. Theme-based analysis of interviews provided contextual information describing why places were selected, complementing the spatial information about where priorities were located. Interviews revealed that lowland areas were not prioritized for conservation due to perceived constraints to working in that zone. Qualitative analysis of interviews also ensured that non-dominant opinions were identified, which revealed trade-offs between prioritizing conservation expansion and maintaining currently protected areas. By coupling participatory mapping with semi-structured interviews, we present an approach that can increase effectiveness for identifying conservation priorities and represent diverse perspectives.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".