The Social Network: How People Infer Relationships From Mutual Connections
Bibliographic record
Abstract
People infer that individuals are socially related if they have overlapping preferences, beliefs, and choices. Here we examined whether people also infer relationships by attending to social network information. In five preregistered experiments, participants were shown the social networks of two target people and their friends or acquaintances within a group, and judged if the targets were socially related to one another. In the first three experiments, adults (total N = 528) were more likely to judge that individuals were friends when a high rather than low proportion of their friendships were mutual. Adults also considered other factors when inferring friendships, such as the number of friends each individual had. In the final two experiments, 5–7-year-olds (total N = 135) were also sensitive to the proportion of mutual relationships. Together, our work suggests that people use proportional information and statistical inferences when assessing whether individuals are socially related.
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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 | 0.000 | 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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 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 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".