Daily technoference, technology use during couple leisure time, and relationship quality
Bibliographic record
Abstract
The landscape of couple leisure time has shifted to include and, in some relationships, rely upon technology use. Technology has the potential to intrude upon face-to-face interactions and quality time together – i.e., technoference, phubbing. However, it is also likely that couples engage in shared technology use, which could lead to bonding. In the current work, we examined one’s own, one’s partner’s, and shared technology use during couple time across 10 days and the potential impacts on couple-time satisfaction, conflict, and relationship quality. We utilized data from 145 couples who completed a baseline online survey and 10 days of daily online surveys concerning leisure time spent together with their partner and their technology use. Multilevel mediational modeling revealed within-person associations between own and partner technology use with daily leisure satisfaction and leisure conflict. Small, but significant within-person indirect effects on daily relationship quality through leisure satisfaction and conflict were also found for own and partner technology use. In other words, results implied a pathway where technology use impacts one’s satisfaction with and conflict during time spent together, and then this (dis) satisfaction and conflict impacts daily relationship quality. Although shared technology use was also a significant predictor, its effects were not robust.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".