Bibliographic record
Abstract
Connecting with others increases happiness, but strangers in close proximity routinely ignore each other. Why? Two reasons seem likely: Either solitude is a more positive experience than interacting with strangers, or people misunderstand the consequences of distant social connections. To examine the experience of connecting to strangers, we instructed commuters on trains and buses to connect with a stranger near them, to remain disconnected, or to commute as normal (Experiments 1a and 2a). In both contexts, participants reported a more positive (and no less productive) experience when they connected than when they did not. Separate participants in each context, however, expected precisely the opposite outcome, predicting a more positive experience in solitude (Experiments 1b and 2b). This mistaken preference for solitude stems partly from underestimating others' interest in connecting (Experiments 3a and 3b), which in turn keeps people from learning the actual consequences of social interaction (Experiments 4a and 4b). The pleasure of connection seems contagious: In a laboratory waiting room, participants who were talked to had equally positive experiences as those instructed to talk (Experiment 5). Human beings are social animals. Those who misunderstand the consequences of social interactions may not, in at least some contexts, be social enough for their own well-being.
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 | 0.005 |
| 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) | 0.003 | 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".