Understanding migration to protected area buffer zones in Costa Rica utilizing cultural consensus analysis
Bibliographic record
Abstract
Human migration to the world’s protected areas’ (PA) buffer zones is widely seen as a significant threat to conserving biodiversity. Research since 2005 has demonstrated some evidence for global migration trends but also highlighted the simultaneous need to understand the local, contextual factors that drive migration around individual PAs. Investigation into human migration patterns to these buffer zones has frequently relied on methods that do not accurately capture the calculus used by migrants in their decisions. The research presented here uses a mixed-methods, cognitive anthropological approach to assess the motivations of Costa Rican migrants to the buffer zones of three national parks. Employing cultural consensus analysis methodology in combination with a demographic analysis based on the Costa Rican census, this study was able to develop important insights into Costa Rican migrant motivations. Importantly, the research finds that there is not a single cultural model among the migrants surveyed regarding conditions driving their decisions. However, data collected indicate significant trends in migrants’ evaluation of critical variables driving decisions, how they relate to one another, and their significance to these migrants. Thus, migrant assessments of the conditions of these variables in both previous and current communities reveal a more complex, contextual picture. This work demonstrates the potential of cognitive anthropological methods to help unpack migrant decision making and help conservation managers understand the factors that drive migration to surrounding communities. The analysis provides further evidence supporting calls for methods that help managers and communities understand the particularities of migration behavior in PA contexts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".