Are You Tired of “Us?” Accuracy and Bias in Couples’ Perceptions of Relational Boredom
Bibliographic record
Abstract
Relational boredom is an important but understudied area of the relationship maintenance literature. In three dyadic studies, we investigated the interplay of accuracy and bias in partners’ perceptions of each other’s relational boredom, and how accurate and biased boredom perceptions were associated with relationship quality. Results revealed that, overall, partners overestimated—but accurately tracked—each other’s relational boredom across the features that comprise relational boredom and across time. Additionally, when people accurately perceived their partner experiencing high levels of boredom, they reported lower relationship quality, but overestimation, underestimation, and accuracy at low levels were associated with high levels of relationship quality. Furthermore, when people accurately perceived their partner experiencing high levels of boredom, their partner also reported lower relationship quality, while only overestimation and accuracy at low levels were consistently associated with higher quality. These findings have important implications for how couples navigate boredom and maintain long-term relationships.
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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 | 0.011 | 0.042 |
| 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 | 0.002 |
| Research integrity | 0.001 | 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".