Bibliographic record
Abstract
This study investigated conceptual and empirical concerns related to trait resilience through systematic review and meta-analysis. First, investigating multiple conceptualizations and operationalizations of trait resilience, we found imprecision and incompatibility in definitions and measures of this construct. Then, based on an analysis of 85 samples comprising 23,983 participants, we assessed whether the eight most commonly used measures of trait resilience in the organizational literature reflected a jingle fallacy (i.e., calling different constructs by the same name) or triangulation (i.e., assessing different aspects of a complex construct) across different measures. The results indicated that the specific measure used to operationalize trait resilience explained between 18% and 85% of the variance in the relationship between trait resilience and its correlates. Moreover, an item-level examination of the measures revealed little consistency across scales and uneven theoretical correspondence with the concept of trait resilience. The findings suggested the presence of a jingle fallacy in the literature. To advance the study of trait resilience and stimulate theoretically informed new research, we used the results to propose a different way of thinking about trait resilience and its measurement.
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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 | 0.450 | 0.680 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".