Network-Related Personality and the Agency Question: Multirole Evidence from a Virtual World
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
The more consistent a person’s network across roles and the more relevant that consistency is for achievement, the more important agency is for understanding network effects on achievement. With network, experience, and achievement data on persons playing multiple characters in a virtual world, evidence is presented to support two conclusions: (1) About a third of network structure is consistent within persons across roles: that is, those who in one role build networks rich in access to structural holes will build similar networks in other roles; builders of closed networks also tend to build that network across roles. (2) Network consistency across roles contributes almost nothing to predicting achievement, which is instead determined by experience and the network specific to the role. The two conclusions are robust across substantively significant differences in the mix of roles combined in a multirole network (too many roles, difficult combination of roles, or roles played to overlapping audiences).
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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