Towards compatibility between artificial and psychometric personality models
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the statistical link between an artificial personality model and the leading psychometric model. Design/methodology/approach An online survey was conducted made of two parts: a 40‐sentence humor appreciation survey corresponding to the artificial personality model, and a 50‐sentence Big 5 psychometric survey. The cross‐correlation between the scales of the two parts was computed, and exploratory factor analysis performed on the Big 5 scores using three different sample age spreads. Findings The cross‐correlation coefficients between the artificial and psychometric personality scores supported the suggestion that there is compatibility between the two, albeit their absolute values were not as high as other studies due to the small sample size. Also, when computing factor analysis on Big 5 scores it was found that the loading of two factors identified as motivational went down systematically with the size of the sample, which empirically supports the suggestion that motivational and cognitive factors are distinct. Research limitations/implications The size of sample was not sufficient to reach a conclusive decision but the evidence was supportive and promising for additional research. Practical implications The compatibility between the artificial and psychometric personality models means that psychometric scores of real persons can be uploaded into artificial personalities in order to mimic real conversation and behaviour. Social implications Improvement of man‐machine interface, facilitating education. Originality/value Correlating the scores of different personality scores is not new, but correlating with artificial personality dimensions defined by humor appreciation scores is new. The suggestion that there is qualitative difference between factors of well established psychometric model is new and could have far reaching implications.
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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 | 0.056 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".