Reappraising Past and Future Transitional Events: The Effects of Mental Focus on Present Perceptions of Personal Impact and Self‐Relevance
Bibliographic record
Abstract
This research examined how instructions to focus on the concrete details (experience focus) versus broader life significance (coherence focus) influence present perceptions of transitional impact and self-relevance for past and future transitional events. Participants (Study 1, N = 119; Study 2, N = 251) selected a past or future transition and wrote about it using either an experiential or coherence focus. Participants then rated the event on transitional impact, self-relevance, and other phenomenological characteristics. Individuals instructed to use a coherence focus on a past transition reported higher levels of material and psychological impact and rated the event as more self-relevant, compared to those instructed to use an experiential focus. The manipulation did not influence ratings for future events. Controlling for temporal distance and emotional valence did not alter the findings. Future transitions were regarded as more personally important than past transitions. Appraisals of the impact and self-relevance of transformative past events (but not future events) are affected by the mental focus adopted at retrieval. The findings are considered in light of essential differences between remembering and forecasting and support the notion that a coherence focus promotes adaptive self-reflection by affording people the cognitive means with which to reconcile transitional experiences.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".