Relational values and empathy are closely connected: A study of residents of Vermont's Winooski River watershed
Bibliographic record
Abstract
Relational values are emerging as an important aspect of ecosystem valuation scholarship and practice. Yet, relatively few empirical examples of their expression exist in the literature. In addition, many characteristics of relational values suggest that they may interact with the quality of empathy, but scholars have not explored that interaction. To address both of these gaps, we designed a semi-structured interview protocol to explore relational values among residents of a large (~28,000 ha) watershed in Vermont, United States of America. We used thematic analysis to explore expressions of relational values and how they may relate to empathy. We discuss how relational values interact with empathy and perspective-taking, as the latter two concepts are theorized in social psychology. In our study, every reference (discrete codable expression) of empathy among our participants co-occurred with a relational-values reference. Conversely, 21% of relational-values references co-occurred with empathy. These results support our proposition that the two concepts are closely related, and we thus argue that there is strong reason to consider empathy as a relational value. We conclude by discussing possible implications of the interaction between relational values and empathy for research and practice, notably their promise for informing the global transformative changes regarding sustainable human–nature relationships called for by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".