Bibliographic record
Abstract
At times, goals seem to conflict, pulling people in opposite directions; at other times, they appear to complement or even facilitate one another, creating harmony. We propose and test a theoretical framework for understanding the antecedents and consequences of perceived goal harmony. We find that goal harmony can be enhanced through the cognitive process of mental integration, which includes identifying connections between goals (e.g., considering how holding a job supports parenting) and creating multifinal means (e.g., considering how a means to job success can also serve parenting). Additionally, goal harmony is acquired through social learning. People in five collectivistic countries reported greater goal harmony than those in five individualistic countries (e.g., more harmony in India and China than in the Netherlands and the United States), and men reported more harmony between their work and family goals than women. We further find that goal harmony predicts and causally increases motivation and well-being. Interventions designed to promote goal harmony enhanced prosocial behaviors and encouraged healthier eating habits. Further, individuals who perceived greater goal harmony were more likely to stick to their New Year's resolutions over a 2-month period. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".