Introducing Relational Values as a Tool for Shark Conservation, Science, and Management
Bibliographic record
Abstract
Relational values are values that arise from a relationship with nature, encompassing sense of place, feelings of well-being (mental and physical health), and cultural, community, or personal identities. With sharks, such values are formed by diverse groups that interact with these animals and their ecosystems, either physically or virtually, whether scientist, student, fisher, or media-consumer. Further, these user groups may overlap or conflict over management plans, media portrayals of sharks, and their conservation status. Although scientists have not explicitly aimed to assess relational values through sharks, qualitative studies of shark fishers, tourism operators, tourists, and the public, as well as historical and archaeological accounts, can be interpreted through an analytical lens to reveal values which can also be defined as relational. To this end, this review considers studies capturing relational values alongside those on economic value (increasingly, sharks’ value is appraised by their financial value by way of shark tourism) and the social and cultural roles of sharks. Based on these studies and the broader relational values literature, we then outline a workflow for how relational values can be leveraged in scientific inquiry, equitable resource management, and education. We conclude that via collaborative assessments of relational values, with implicit inclusion of all values from sharks and acknowledgement of their importance of to all parties involved in user conflicts, the relational values framework can lead to constructive dialogue on polarizing conservation and management issues. By illuminating shared values, and/or revealing dichotomies of values ascribed towards certain areas or objects, this framework can provide inroads to mediation, seeking to conserve or even restore relationships with nature and their derived values insomuch as is possible. This approach can yield unexpected knowledge, solutions, and compromises in an increasingly complex conservation landscape.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| 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".