Bibliographic record
Abstract
People are motivated to behave selfishly while appearing moral. This tension gives rise to 2 divergently motivated selves. The actor-the watched self-tends to be moral; the agent-the self as executor-tends to be selfish. Three studies present direct evidence of the actor's and agent's distinct motives. To recruit the self-as-actor, we asked people to rate the importance of various goals. To recruit the self-as-agent, we asked people to describe their goals verbally. In Study 1, actors claimed their goals were equally about helping the self and others (viz., moral); agents claimed their goals were primarily about helping the self (viz., selfish). This disparity was evident in both individualist and collectivist cultures, attesting to the universality of the selfish agent. Study 2 compared actors' and agents' motives to those of people role-playing highly prosocial or selfish exemplars. In content (Study 2a) and in the impressions they made on an outside observer (Study 2b), actors' motives were similar to those of the prosocial role-players, whereas agents' motives were similar to those of the selfish role-players. Study 3 accounted for the difference between the actor and agent: Participants claimed that their agent's motives were the more realistic and that their actor's motives were the more idealistic. The selfish agent/moral actor duality may account for why implicit and explicit measures of the same construct diverge, and why feeling watched brings out the better angels of human nature.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".