Validation of the Narrative Emplotment Scale and its correlations with well‐being and psychological adjustment
Bibliographic record
Abstract
Two studies examined correlates of the Narrative Emplotment Scale (NES), which measures the extent to which individuals perceive chance events and unchosen experiences as meaningfully connected. In Study 1 (N=99), the NES demonstrated adequate test-retest stability and good internal reliability. The scale was positively related to paranormal beliefs, mystical experiences, and absorption. In Study 2 (N=342), personality measures indicative of external locus of control, intrinsic religiosity, well-being, satisfaction with life, and a measure of frequency of coincidence experience were all positively correlated with narrative emplotment, providing further support for the construct validity of the scale. In terms of the question of whether meaning making is predictive of better or worse psychological adjustment, analyses indicated that the relationship between narrative emplotment and psychological adjustment was moderated by individual differences in coping strategies. Path analysis indicated that emplotment was a mediator of the pathway between religiosity and well-being. Emplotment had a negative effect on well-being through chance locus of control. These analyses suggest that this type of meaning-making is an important variable for understanding religious/spiritual beliefs and their influence on psychological adjustment.
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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 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".