Bibliographic record
Abstract
Wild animals are important worldwide because of the multiple values they represent for human societies. Different frameworks have been proposed to understand the values of wildlife from economic and noneconomic perspectives. Despite efforts from different disciplines to provide a holistic framework for the analysis of wildlife values, the focus is still based on the monetary value derived from market prices. Community-oriented approaches to wildlife conservation have an especially strong economic rationale because they depend on the economic costs and benefits that wildlife represents to local communities. However, purely economic approaches ignore that values are subjective and as such are perceived differently among stakeholders according to their social, economic, cultural, and ecological context. The lack of a holistic framework hinders the possibility to provide a clear and practical tool for the resolution of wildlife conservation conflicts and the identification of management options that maximize values. Based on a wide literature review, we propose a comprehensive wildlife value framework (WVF) incorporating the values of wildlife identified in the academic literature into the total economic value (TEV) framework. Costs associated with human-wildlife conflicts are also incorporated as well as subjective perceptions of values based on multidimensional well-being criteria. This work aims to provide a common structure within which different perspectives related to wildlife can be captured to inform multi-actor, multi-objective decision making related to wildlife management.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".