The network structure of the VIA-120 inventory of strengths: an analysis of 1,255,248 respondents
Bibliographic record
Abstract
Traditional factor analyses used to analyze the structure of psychological strengths have yielded different solutions, not always confirming the original structure of 24 strengths corresponding to six virtues as proposed by Peterson and Seligman in their initial model. In contrast with previous factorial approaches, this study used network analysis to explore the map of strengths, assessed with the VIA Inventory of Strengths (VIA-IS), in a large sample of individuals (N = 1,255,248) from the general population in the United States, Australia, Canada, and UK. The network analysis revealed four different communities (i.e., groups of strengths): Discernment, Interpersonal, Responsibility, and Energy. The strength most connected to other strengths was Gratitude, whereas Love of Learning was the least connected node. These results open a new way to conceptualize psychological strengths as a complex network of mutually interconnected strengths. These findings complement results from factor analyses that future research should replicate and validate.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".