Subjective Impact, Meaning Making, and Current and Recalled Emotions for Self‐Defining Memories
Bibliographic record
Abstract
Two studies examined the impact of self-defining events on individuals (i.e., subjective impact), meaning making with regard to these events, and how subjective impact may account for the pattern of current and recalled emotions for these self-defining memories (Singer & Moffitt, 1991-1992). In Study 1, participants recalled self-defining memories, indicating how much impact the recalled events have had on them and described meaning making for these events. Subjective impact was shown to be a good marker for meaning making. Participants in Study 2 each recalled five self-defining memories, reporting their current emotions about the events, the emotions they recalled feeling at the time, and the impact the events have had on them. As expected, for negative memories, people reported less negative emotion (e.g., sadness) and more positive emotion (e.g., pride) compared to how they recalled feeling at the time. For positive memories, people reported equally intense positive emotion (e.g., love) and less negative emotion (e.g., fear) compared to how they recalled feeling at the time. These patterns of current and recalled emotions were accounted for by impact ratings.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".