The psychology of appraisal: Specific emotions and decision‐making
Bibliographic record
Abstract
Abstract A growing stream of research has examined emotions and decision‐making based on the appraisal tendencies associated with emotions. This paper outlines two general approaches that can lead to further our understanding of the variety of ways emotions affect decision‐making and information processing. Specifically, future research can examine the nature of emotional appraisals or investigate the nature of decision contexts and underlying psychological processes influenced by emotions. To understand the nature of emotional appraisals, scholars could examine the interaction of two appraisal dimensions or identify novel appraisal tendencies. To understand the decision‐making contexts and psychological processes influenced by emotions, scholars could examine how emotions interact with contextual influences to shape judgments through a variety of processes such as providing information, priming goals, or activating mindsets. These approaches to the study of emotions and decision‐making will contribute to more nuanced theory development around emotions, nurture new empirical work, and encourage interest in exploring a broader set of emotions.
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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 | 0.002 | 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 | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".