Social value at a distance: Higher identification with all of humanity is associated with reduced social discounting
Bibliographic record
Abstract
How much we value the welfare of others has critical implications for the collective good. Yet, it is unclear what leads people to make more or less equal decisions about the welfare of those from whom they are socially distant. The current research sought to explore the psychological mechanisms that might underlie welfare judgments across social distance. Here, a social discounting paradigm was used to measure the tendency for the value of a reward to be discounted as the social distance of its recipient increased. Across two cohorts (one discovery, one replication), we found that a more expansive identity with all of humanity was associated with reduced social discounting. Additionally, we investigated the specificity of this association by examining whether this relationship extended to delay discounting, the tendency for the value of a reward to be discounted as the temporal distance to its receipt increases. Our findings suggest that the observed association with identity was unique to social discounting, thus underscoring a distinction in value-based decision-making processes across distances in time and across social networks. As data were collected during the COVID-19 pandemic, we also considered how stress associated with this global threat might influence welfare judgements across social distances. We found that, even after controlling for COVID-19 related stress, correlations between identity and social discounting held. Together these findings elucidate the psychological processes that are associated with a more equal distribution of generosity.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".