Endorsing help for others that you oppose for yourself: Mind perception alters the perceived effectiveness of paternalism.
Bibliographic record
Abstract
How people choose to help each other can be just as important as how much people help. Help can come through relatively paternalistic or agentic aid. Paternalistic aid, such as banning certain foods to encourage weight loss or donating food to alleviate poverty, restricts recipients' choices compared with agentic aid, such as providing calorie counts or donating cash. Nine experiments demonstrate that how people choose to help depends partly on their beliefs about the recipient's mental capacities. People perceive paternalistic aid to be more effective for those who seem less mentally capable (Experiments 1 and 2), and people therefore give more paternalistically when others are described as relatively incompetent (Experiment 3). Because people tend to believe that they are more mentally capable than are others, people also believe that paternalistic aid will be more effective for others than for oneself, effectively treating other adults more like children (Experiments 4a-5b). Experiencing a personal mental shortcoming-overeating on Thanksgiving-therefore increased the perceived effectiveness of paternalism for oneself, such that participants thought paternalistic antiobesity policies would be more effective when surveyed the day after Thanksgiving than the day before (Experiment 6). A final experiment demonstrates that the link between perceived effectiveness of aid and mental capacity is bidirectional: Those receiving paternalistic aid were perceived as less mentally capable than those receiving relatively agentic aid (Experiment 7). Beliefs about how best to help someone in need are affected by subtle inferences about the mind of the person in need. (PsycINFO Database Record
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".