Evaluating Others’ Relevance: Dimensions, Levels, and Interdependence
Bibliographic record
Abstract
Humans seek to determine what other people are like and use others’ characteristics—such as their age, gender, and attractiveness—to assess others’ relevance to their own goals. The resulting appraisals of others’ opportunity and threat shape social judgments and behavior. My thesis accomplishes three broad aims. With Studies 1 and 2, I demonstrate that appraisals of who threatens and who facilitates one’s goals are independent dimensions of social judgment (Lassetter et al., 2021). People can be evaluated as (a) facilitating a goal, (b) threatening a goal, (c) both facilitating and threatening a goal, or (d) neither facilitating nor threatening a goal. I term this two-dimensional model the Relevance Appraisal Matrix. Relevance appraisals shift dynamically with perceiver goals: For example, a person may be appraised as facilitating a mate-seeking goal, but as neither facilitating nor threatening a self-protection goal (Lassetter et al., 2021). Further, relevance appraisals are distinct from stereotypes of group attributes and likely act as a mechanism between stereotypes of others and downstream outcomes. With Studies 3 and 4, I show that the Relevance Appraisal Matrix describes appraisals of individual targets (e.g., a nurse, a man, a close friend) and groups (e.g., medical professionals, men). Thus, in addition to appraising whether individual targets—including close and known others—pose opportunity and/or threat, relevance appraisals function as group-level phenomena that influence evaluations of broader groups. With Studies 5 and 6, I examine the link between relevance and interpersonal closeness. Close others may be especially likely to influence decisions and goal pursuit—and possibly, more likely to both facilitate and threaten goals. I show that threat and opportunity appraisals negatively and positively relate to perceived closeness, respectively. In sum, I propose a framework for understanding the structure of relevance, test how that framework applies to individual- and group-level social perception, and examine the link between relevance and closeness. Relevance appraisals are central and consequential to social perception. My thesis illuminates the structure of these appraisals and lays the foundation for future research examining how relevance shapes attention, emotions, and behavior.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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 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".