Cooperation and conflict in romantic partners’ personal projects: the role of life domains
Bibliographic record
Abstract
Personal projects represent a person's pursuits in different life domains. The present study examines the orientations of adults' personal projects and how these orientations are embedded in the dynamics of romantic relationships. Cross-sectional data from 249 married or cohabitating Hungarian heterosexual couples were collected (mean age 42 ± 10.76 and 39.64 ± 10.21 years for male and female partners, respectively). An adapted version of the Personal Project Assessment procedure was completed by both partners individually. Four of their chosen projects were evaluated based on perceived cooperation and conflict regarding these projects and other predefined aspects. First, after applying a person-oriented approach, four meaningful content domains emerged from the thematically coded data using cluster analysis: (1) Practical, (2) Work-Life Balance, (3) Relationships, and (4) Learning and Growth orientations. For both genders, people with Learning and Growth orientation were younger than those with Practical orientation, and among women, the Work-Life Balance orientation group was older. Second, we linked the content domains to relationship experiences on the dyadic level. Both partners with Learning and Growth orientation goals perceived less cooperation. Female partners whose spouses had Work-Life Balance or Learning and Growth orientation goals perceived less conflict regarding their own goals. Overall, Learning and Growth-oriented goals can be considered more distant from the dynamics of romantic relationships because they involve fewer joint experiences and less cooperation and conflict.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".