Bibliographic record
Abstract
Experiencing positive emotion is often linked with greater psychological health and lower psychopathology. However, a growing body of research suggests a surprising paradoxical effect: in spite of the benefits of experiencing positive emotion, there may be important downsides to pursuing positive emotion. This chapter reviews current findings on the paradoxical effects of pursuing positive emotion (often focusing on the specific emotion of happiness), discusses possible mechanisms to explain these paradoxical effects, and suggests methods to avoid these effects. Specifically, the chapter outlines three key mechanisms for the paradoxical effects of pursuing happiness: First, as people pursue happiness, they tend to set high standards for their happiness which can result in disappointment. Second, when people are inaccurate about how to achieve happiness, they may engage in activities that are counterproductive for achieving happiness and psychological health. Third, as people pursue happiness, they may monitor their experience of happiness which can directly interfere with the experience of happiness. These processes, in turn, may create risk for psychopathology. Fortunately, these three mechanisms also suggest how to avoid paradoxical effects of pursuing happiness: by removing impossibly high standards, disappointment can be avoided; by engaging in productive happiness pursuits, people can attain more sustainable happiness; and by automatizing the process of pursuing happiness, the ill-effects of monitoring can be avoided. Although pursuing happiness can paradoxically lead to reduced happiness and greater psychopathology, by understanding the mechanisms underlying this paradox, we can obtain valuable insights into effective ways to achieve happiness and avoid psychopathology.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".