What Do We Know When We Know a Person<i>Across Contexts</i>? Examining Self‐Concept Differentiation at the Three Levels of Personality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it