How Romantic Partners Communicate About Sacrifice: A Theoretical Framework and Measurement Approach
Bibliographic record
Abstract
Romantic partners regularly make sacrifices for each other, yet little is known about how they communicate about sacrifice. We developed the Sacrifice Communication Scale to capture seven parallel communication types for sacrificers (rational, affectionate, positive reframing, downplaying, victimizing, hostile, withdrawal) and recipients (rational, appreciative, positive reframing, undeserving, critical, withdrawal). Across a cross-sectional study (N = 463 sacrificers and 469 recipients), a weekly experience sampling study (N = 193), and a dyadic longitudinal study (N = 190 couples), sacrificers’ affectionate and recipients’ appreciative communication predicted higher relationship quality through increased intimacy, whereas sacrificers’ victimizing, sacrificers’ hostile, and recipients’ critical communication predicted lower relationship quality, primarily through increased conflict. Sacrificers’ and recipients’ chronic withdrawal also predicted lower relationship quality through reduced intimacy. These findings highlight how sacrifice communication shapes relational outcomes, positioning communication as a key way for couples to navigate sacrifices constructively and sustain relationship quality.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".