Spatial proximity in relationships research methods: The effect of partner’s presence during survey completion on shared reality in romantic couples’ daily lives
Bibliographic record
Abstract
Spatial proximity may be an artifact of relationships research methodology; however, little work has explored how this feature of research designs influences perceptions of one’s relationship, particularly shared reality (i.e., experiencing a commonality of inner states). The present research tested whether spatial proximity would independently contribute to shared reality in couples’ daily lives. In 2 daily diary studies, each across 3–4 weeks (N 1 = 76 couples, 3694 observations; N 2 = 84 couples, 3073 observations), participants indicated whether or not their partner was spatially proximal, and also completed measures of shared reality and relationship satisfaction. Spatial proximity to one’s partner resulted in higher shared reality on the day of the survey completion and predicted increases in shared reality from the previous day, but this effect did not spillover into the following day. These findings held controlling for conflict, shared daily experiences (e.g., cooking together), and shared survey experiences (i.e., whether they completed the survey at the same time). In addition, this effect was unique to shared reality, whereby spatial proximity did not predict relationship satisfaction. However, shared reality was associated with increases in relationship satisfaction across the daily diary period. Thus, researchers should consider spatial proximity when developing their research design as it may influence shared reality, which has implications for relationship well-being.
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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 | 0.049 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".