Fostering ocean empathy through future scenarios
Bibliographic record
Abstract
Abstract Empathy for nature is considered a prerequisite for sustainable interactions with the biosphere. Yet to date, empirical research on how to stimulate empathy remains scarce. Here, we investigate whether future scenarios can promote greater empathy for the oceans. Using a pre‐post empathy questionnaire, participants ( N = 269) were presented with an optimistic or a pessimistic future scenario for the high seas in a virtual reality (VR) or written format. Results showed that post‐test empathy levels were significantly higher than pre‐test levels, indicating that future scenarios fostered ocean empathy. We also find that the pessimistic scenario resulted in greater empathy levels compared to the optimistic scenario. Finally, we found no significant difference between the VR and written conditions and found that empathy scores significantly decreased 3 months after the initial intervention. As one of the first studies to empirically demonstrate the influence of a purposeful intervention to build ocean empathy, this article makes critical contributions to advancing research on future scenarios and offers a novel approach for supporting ocean sustainability. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".