Fostering ocean empathy through future scenarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it