What Do We Know When We Know a Person<i>Across Contexts</i>? Examining Self‐Concept Differentiation at the Three Levels of Personality
Bibliographic record
Abstract
OBJECTIVE: Previous research examining self-concept differentiation (SCD) has been characterized by (a) a focus on behavioral traits and (b) the conflation of mean-level and inter-contextual differentiation. In two studies, we considered non-conflated measures of SCD at the three levels of personality description in relation to adjustment. METHOD: In Study 1, participants completed measures of adjustment, rated their behavioral tendencies (dispositional traits), produced a list of goals (characteristic adaptations), and recalled a self-defining memory (life narratives), from within professional and personal domains. In Study 2, the procedure was modified: Participants reporting either low or high levels of adjustment subsequently rated their behavioral traits, provided a list of goals, or produced a self-defining memory, from five contexts. RESULTS: In Study 1, adjustment related positively to SCD at the level of characteristic adaptations but negatively to SCD at the level of life narratives. In Study 2, well-adjusted participants exhibited a greater degree of SCD at the level of characteristic adaptations but a greater degree of thematic consistency at the level of life narratives, relative to those low in adjustment. CONCLUSIONS: These results highlight the dynamic nature of SCD across levels of personality and align with the notion that differentiation represents virtue and vice.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".