How Sacrifice Impacts the Giver and the Recipient: Insights From Approach‐Avoidance Motivational Theory
Bibliographic record
Abstract
This study investigated how sacrificing for approach versus avoidance goals shapes the giver's and the recipient's emotions and relationship quality. A sample of 80 dating couples participated in a three-part study in which they discussed sacrifice in the laboratory (Part 1), reported on their daily sacrifices for 14 days (Part 2), and completed a follow-up survey 3 months later (Part 3). When partners discussed a sacrifice they had made for approach goals, they experienced greater relationship quality, whereas when they discussed a sacrifice they had made for avoidance goals, they experienced poorer relationship quality. These effects were replicated with outside observer reports. On days when partners sacrificed for approach goals, both partners experienced increased relationship quality, but on days when people sacrificed for avoidance goals, the giver experienced decreased relationship quality. These effects were mediated by positive and negative emotions, respectively. Approach sacrifice goals predicted increases in relationship quality and avoidance sacrifice goals predicted decreases in relationship quality, as reported by both partners 3 months later. Sacrifice per se does not help or harm relationships, but the goals that people pursue when they give up their own interests can critically shape the quality of intimate bonds.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".