Conservationists, hunters and farmers: the <scp>E</scp>uropean rabbit <scp><i>O</i></scp><i>ryctolagus cuniculus</i> management conflict in the <scp>I</scp>berian <scp>P</scp>eninsula
Bibliographic record
Abstract
Abstract Biodiversity conflicts arise when the interests of different stakeholders over common resources compete. Typically, the more parties involved, the more complex situations become. Resolution of biodiversity conflicts requires an understanding of the ecological, social and economic factors involved, in other words the interests and priorities of each stakeholder. However, in most biodiversity conflicts, many of these components remain poorly understood. As a case study, we analyse the conflict involving conservationists, hunters and farmers in the management of a native lagomorph, the European rabbit Oryctolagus cuniculus, in the Iberian Peninsula. We review the socio‐economic context of the rabbit management conflict, investigating the roles of the main stakeholders involved in the conflict and evaluating the ecological, economic and social factors that motivate it. We provide management directions for the short‐term amelioration of the conflict and discuss some long‐term perspectives. Overall, the interests of conservationists, hunters and farmers depend on the specific scenario where the conflict takes place. A deeper understanding of the human dimensions of the conflict will help in the design of an appropriate management model to solve this biodiversity conflict in the Iberian Peninsula.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".