Interpersonal Expectations as the Building Blocks of Social Cognition: An Interdependence Theory Perspective
Bibliographic record
Abstract
In this paper I use interdependence theory as an analytic framework for depicting the logically interconnected network of expectations that determines social interaction. The framework focuses on expectations about a partner’s goals (B) relevant to particular interdependence situations (S), and suggests that expectations about these two elements define the social situation that activates a person’s own goals (A). Together, these elements determine interaction behavior (I). This SABI framework is complementary to Mischel and Shoda’s (1995) CAPS theory of personality in its logic. It depicts a person’s interpersonal dispositions as having profiles or signatures dependent on both the expected features of situations and the expected dispositions of partners. A taxonomic theory for classifying both situations and the functionally relevant goals of interaction partners is outlined. Research on attachment theory and trust is used to illustrate the model. Finally, I suggest that people’s expectations about partners’ prosocial motivations—their perceived responsiveness toward the self—play an imperial role in social cognition, and, further, that complex SABI models can be seen as detailing a set of security operations that serve as a program for social action. SABI models detail the set of mechanisms that constitute the basic survival kit of interpersonal relations.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".