A network‐analytic perspective on 30 years of personality research
Bibliographic record
Abstract
Abstract The current research adopted a network‐analytic approach to summarize the changes within the field of personality psychology over a 30‐year span from 1990 to 2019. Bibliographic data from 25,238 articles were used to construct three separate co‐authorship networks respectively representing the patterns of collaboration within each decade of personality research (i.e., 1990–1999, 2000–2009, and 2010–2019). The network properties of each co‐authorship graph suggested that personality researchers have become more interconnected and collaborative with each successive decade. An examination of the semantic content of these articles suggested that the synthesis of clinical and normative trait models, along with the integration of traditional person and situation perspectives, may be driving the increased connectivity and collaboration between researchers. We hope that this novel application of network‐analytic and machine‐learning principles can serve as proof of concept for future efforts to summarize a scientific literature.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".