Who gives? Characteristics of those who have taken the <i>Giving What We Can</i> pledge
Bibliographic record
Abstract
OBJECTIVE: In the current project, we focus on another group of unusual altruists: people who have taken the Giving What We Can (GWWC) pledge to donate at least 10% of their income to charity. Our project aims to understand what is unique about this population. BACKGROUND: Many people care about helping, but in recent years there has been a surge of research examining those whose moral concern for others goes far beyond that of the typical population. These unusual altruists (also termed extraordinary or extreme altruists or moral exemplars) make great personal sacrifices to help others-such as donating their kidneys to strangers or participating in COVID-19 vaccine challenge trials. METHOD: In a global study (N = 536) we examine a number of cognitive and personality traits of GWWC pledgers and compare them to a country-matched comparison group. RESULTS: In accordance with our predictions, GWWC pledgers were better at identifying fearful faces, more morally expansive and higher in actively open-minded thinking, need for cognition and two subscales of utilitarianism and, tentatively, lower in social dominance orientation. Against our predictions, they were lower in maximizing tendency. Finally, we found an inconclusive relationship between pledger status and empathy/compassion that we believe warrants further examination. CONCLUSIONS: These findings offer initial insights into the characteristics that set apart those who have made the decision to donate a substantial portion of their income to help others.
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 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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".