How Can People Become Happier? A Systematic Review of Preregistered Experiments
Bibliographic record
Abstract
Can happiness be reliably increased? Thousands of studies speak to this question. However, many of them were conducted during a period in which researchers commonly “ p-hacked,” creating uncertainty about how many discoveries might be false positives. To prevent p-hacking, happiness researchers increasingly preregister their studies, committing to analysis plans before analyzing data. We conducted a systematic literature search to identify preregistered experiments testing strategies for increasing happiness. We found surprisingly little support for many widely recommended strategies (e.g., performing random acts of kindness). However, our review suggests that other strategies—such as being more sociable—may reliably promote happiness. We also found strong evidence that governments and organizations can improve happiness by providing underprivileged individuals with financial support. We conclude that happiness research stands on the brink of an exciting new era, in which modern best practices will be applied to develop theoretically grounded strategies that can produce lasting gains in life satisfaction.
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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 | 0.025 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".