Bridging people and perspectives: General and language-specific social network structure predict mentalizing across diverse sociolinguistic contexts.
Bibliographic record
Abstract
Mentalizing, or reasoning about others' mental states, is a dynamic social cognitive process that aids in communication and navigating complex social interactions. We examined whether exposure to diverse perspectives, afforded by occupying influential social network positions, predicted bilingual adults' performances on a behavioral mentalizing rating task in regions of high and low linguistic diversity. We calculated the degree to which respondents' social network position generally bridged unconnected others (i.e., general betweenness) and specifically bridged language communities (i.e., language betweenness). General betweenness predicted mentalizing performance regardless of region, whereas language betweenness only predicted mentalizing in a high linguistic diversity region, where bilingualism is ubiquitous and mentalizing to resolve perspective differences on the basis of language may be an adaptive cognitive strategy. These results indicate that human cognition is sensitive to social context and adaptive to the sociolinguistic demands of the broader environment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".