Using Multi-Informant Ratings to Distinguish Generality and Context Specificity in Personality
Bibliographic record
Abstract
Distinguishing generality and context specificity in personality is a one of the grand challenges of psychology. Extant research attempts to tackle this challenge by examining the agreement (or, consensus) between informants who rate targets’ personality traits in different contexts (e.g., the consensus between family and friends), but this literature is highly fragmented. Accordingly, we present a new theoretical framework that synthesizes theories of person-situation interaction into three parameters whereby contexts influence personality expression and perception: Adherence (i.e., generality), adaptation (i.e., context specificity), and acuity (i.e., perceptual accuracy). We then test this framework by examining cross-context consensus among informant ratings of Big Five personality traits in a meta-analysis (k = 43 samples, Ntargets = 6,363) spanning four contexts (i.e., family, friends, colleagues, strangers) and a primary sample (Ntargets = 6,120) spanning four workplace roles (i.e., supervisors, peers, subordinates, clients). Across traits and contexts, cross-context consensus is moderate among informant ratings of personality (mean r = .24). Consensus is stronger when informants rate extraversion or when they are well-acquainted with targets (i.e., family, friends, colleagues), but it is relatively homogenous across workplace roles. Surprisingly, the primary obstacle to consensus is not inconsistent behavior by targets, but informants who are error-prone in their personality ratings. After correcting for this error, we find that approximately 80% of targets’ personality expressions generalize across contexts and 20% are context specific. We conclude by discussing contributions and implications of findings, as well as limitations and future research directions.
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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 | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".