You seem tired, but so am I: Willpower theories and intention to provide support in romantic relationships
Bibliographic record
Abstract
Although providing support in romantic relationships is important for the well-being of both partners, providing support can be effortful. People have varying implicit theories about the exertion of effort; limited willpower theorists believe that mental resources become exhausted with use, while nonlimited willpower theorists believe that exerting effort can even prepare you for future exertion. While limited willpower theorists are more likely to experience depletion and limitations themselves, they may also be more likely to perceive and empathize with the depletion and limitations of their romantic partners. We conducted a daily diary study ( N = 363; 1,429 observations) to examine how willpower theories relate to participants’ intentions to support their romantic partners in the evenings. We find that limited theorists report their partners as more tired (predicting more intention to support)—however, limited theorists also report more fatigue and lower mood themselves (predicting less intention to support). Overall, limited willpower beliefs were associated with less, not more, intent to support one’s partner for the rest of the evening. Even if limited willpower theories improve people’s abilities to perceive their partner’s fatigue, at the end of the day, they may not feel they have the mental resources to support their romantic partners.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".