Subjectifying the Personality State: Theoretical Underpinnings and an Empirical Example
Bibliographic record
Abstract
Recent developments in personality research highlight the value of modelling dynamic state–like manifestations of personality. The present work integrates these developments with prominent clinical models addressing within–person multiplicity and promotes the exploration of models centred on state–like manifestations of personality that function as cohesive organizational units. Such units possess distinct subjective qualities and are characterized by specific affects, behaviours, cognitions, and desires that tend to be co–activated. As background, we review both theory and research from the fields of social cognition, psychotherapy, and psychopathology that serve as the foundation for such models. We then illustrate our ideas in greater detail with one well–supported clinical model—the schema therapy mode model, chosen because it provides a finite and definite set of modes (i.e. cohesive personality states). We assessed these modes using a newly developed experience–sampling measure administered to 52 individuals (four times daily for 15 days). We estimated intraindividual and group–level temporal and contemporaneous networks based on the within–person variance as well as between–person network. We discuss findings from exemplar participants and from group–level networks and address cross–model particularities and consistencies. In conclusion, we consider potential idiographic and nomothetic applications of subjective states dynamic personality research based on intensive longitudinal methods. © 2020 European Association of Personality Psychology
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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 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".