Threats and opportunities: Independent dimensions of goal relevance shape social cognition and behavior.
Bibliographic record
Abstract
People pursue goals. They seek to build friendships, find romantic partners, maintain close relationships, gain social status and resources, and stay healthy and safe. But pursuing goals requires assessing who, among the people around them, will help or hurt their ability to reach those goals-that is, who poses goal-relevant affordances. This article overviews recent advances and new predictions from an affordance management approach to social cognition and behavior. The central tenet of this work is that judgments of who helps or hurts goals are independent (rather than opposite ends of a single judgment): Who helps my goal, and who hurts my goal? For any goal, people judge others in one of four ways: as helping the goal, hurting the goal, both helping and hurting the goal, or as irrelevant to the goal. These perceived affordances change across goals: people who help one goal may hurt, both help and hurt, or be irrelevant to another goal. This simple, novel division of helping and hurting across goals has numerous implications for psychological phenomena. It provides a framework for understanding when and how two forms of devaluation will emerge-being seen to pose a threat and being seen as irrelevant-with implications for prejudice, stigmatization, and discrimination. It also provides a lens for understanding how and when others' appraisals of us may affect our own goal pursuit. The article concludes by discussing necessary next steps and promising new directions for applying this approach to understand social cognition and behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".