A discourse on the use of machine learning (ML) in personality psychology: Can we expect ML to predict questionnaire scores from idiographic text-based data?
Bibliographic record
Abstract
This paper explores Machine Learning’s (ML) potential to predict motives and personality dispositions from text-based data, aligning with McAdams’ framework on layers of personality. ML-predicted scores demonstrated no significant advantage over a baseline model that consistently predicted the median of the motives or personality dispositions. Possible factors discussed include unmet ML algorithm requirements, unsuitability of collected texts for predicting motives and dispositions, and ML’s limitations in capturing contextualized and implicit aspects of personality. We discuss life narrative research and practice in relation to the nomothetic-idiographic debate and advocate for personality research to incorporate context-specificity and idiosyncrasy. From a social constructionist perspective, we envision future research – though not yet practice – on counselling processes delivered or supported by Generative AI (GenAI).
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 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".