Emotional Aftereffects: Some Negative Consequences and Thoughts on How to Avoid Them
Bibliographic record
Abstract
In Chapter 7, we laid out a model that begins to describe how multiple motives are integrated. The justice motive theory is probably unique among dual-process theories in proposing that people in every encounter initially engage in preconscious processing of cues that define who deserves what from whom. If those encoded cues indicate that a person's deservingness is violated or in jeopardy they will automatically elicit justice imperatives: emotion-directed efforts to correct the injustice. Subsequent to this initial response, the person may engage in thoughtful, norm-dominated processing of motivationally relevant salient cues. In order for this secondary controlled processing to occur, there must be sufficient cognitive resources remaining after the person's initial automatic responses for him or her to attend to, and process, salient incentives and alternative courses of action: the greater the salient incentives and subsequent thoughtful deliberations, the greater the probability that some form of normatively appropriate self-interest rather than a justice imperative will shape the person's decisions and behavior. It is obvious and important that people are often able to exert self-control and arrive at “wise” decisions concerning the most enlightened rational courses of action even while they are experiencing the presence of emotion-laden imperatives. The weaker the initial arousal and the more serious the perceived outcomes at stake, the greater the time and efforts employed to arrive at a wise or at least reasonable response.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".