CAPTION-ing the situation: A lexically-derived taxonomy of psychological situation characteristics.
Bibliographic record
Abstract
In comparison with personality taxonomic research, there has been much less advancement toward establishing an integrative taxonomy of psychological situation characteristics (similar to personality characteristics for persons). One of the main concerns has been the limited content coverage of the characteristics being used. To address this issue, we present a collection of 4 lexically based studies using the largest-to-date number of situation characteristics to identify the major dimensions of the psychological situation. These studies each implemented a unique sampling and analytic methodology-namely, a qualitative dimensional exploration; the factor analyses of 2, independent samples of large-scale in situ ratings of situations; and the use of lexical-vector representations from neural-network-based models derived from millions of sources of natural-language usage with a total of 146.7 billion words. Across these studies, a clear 7-dimensional structure emerged: Complexity, Adversity, Positive Valence, Typicality, Importance, Humor, and Negative Valence-collectively referred to as the "CAPTION" model, which parsimoniously integrates the diversity of dimensions found in the extant literature. We then introduce both full- and short-form measures of these CAPTION. Data from 2 additional diverse samples of native English speakers suggest that the measures have good psychometric properties, and are able to predict a broad range of important psychological outcomes (e.g., behaviors, affect, motivation, and need satisfaction), even when pitted against extant situation taxonomic frameworks. We conclude by discussing how the CAPTION framework may serve as a useful tool for conceptualizing and measuring a broad range of psychological situations across all areas of psychology. (PsycINFO Database Record
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".