The network structure of the VIA-120 inventory of strengths: an analysis of 1,255,248 respondents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it