MEMORIES AND FORECASTS IN CLOSE RELATIONSHIPS: A CROSS-CULTURAL INVESTIGATION
Bibliographic record
Abstract
Interpersonal memories and forecasts can be influenced (or shaped) by individuals’ current relationship experiences. The present research examined cultural differences in interpersonal memories and forecasts, situated in a current/recent positive or negative interpersonal context. Significant cultural differences were observed in the negative, but not positive, interpersonal context. When a current/recent negative relationship event was made salient, Euro-Canadians brought to mind more negative memories (Studies 1 to 3), and generated more negative forecasts (Study 4), about their close other than Chinese did. This was true regardless of whether the current event was a hypothetical scenario or an actual real-life situation. Furthermore, cultural differences in interpersonal memories and forecasts in the negative condition were mediated by focal thinking, which is the extent to which individuals think about and focus on their current negative interpersonal experience (Studies 3 and 4). These negative relational memories and forecasts were associated with poorer perceived relationship quality, lower willingness to help a close other, and less forgiveness. In particular, when a close other did something that was hurtful or wrong, Euro-Canadians perceived their relationship quality to be poorer than did Chinese, partly due to the negative thoughts that came to their mind. The present findings highlighted the role of focal thinking in the way people recall and forecast interpersonal events in a current negative relational context, and further demonstrated that cultural differences in the accessibility of memories and forecasts were not attributable to potential alternative explanations such as relationship-harmony maintenance or non-linear thinking styles.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".