Conservation conflict hotspots: Mapping impacts, risk perception and tolerance for sustainable conservation management
Bibliographic record
Abstract
Global processes manifesting as activities in local places have led to an increase in documented conservation conflicts. Conservation conflicts are sometimes labelled human-wildlife conflict, focusing only on the direct negative impact of species (usually wildlife) on humans or vice versa. However, many authors now recognize that conservation conflicts arise between people with diverse views, when one party acts against the interests of another. They are thus human-human conflicts and not merely an impact on or from conservation. Conflict is not always directly correlated with impact because perceptions of risk, levels of tolerance and conservation values influence human responses. This review aims to define the concept of ‘conservation conflict hotspots’ and explore its practical applications in conservation. We propose that the interaction of impact, risk perception, level of tolerance in a context of conservation values can be mapped at a local scale, with spatial visualization assisting the prediction, understanding and management of such hotspots. The term conservation value incorporates measures of indigeneity, endemicity and demography along with emotional or cultural attachment to species or places. The umbrella terms of risk perception and tolerance capture many of the aspects of attitude, values and individual demographics that can influence people’s actions, enabling contextualization of relevant social factors at local scales. Spatially mapped layers enable us to plan and target conservation efforts towards human as well as ecological factors. The concept of ‘conservation conflict hotspot’ emphasizes the need for transdisciplinary research to understand underlying drivers of conflict and for dialogical and peace-building approaches to facilitate trust and cooperation amongst actors. We can thus address conflicts and achieve sustainable outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".