Bibliographic record
Abstract
This chapter discusses the contextualization of human traits in situations. Most of the research on contextualizing traits has, up to now, been centered on personality traits. Therefore, much of the examination is on how personality traits relate to situations, but it extrapolates those findings to virtues and discusses theory and research related to the contextualization of virtue traits. In the exploration of trait contextualization, the chapter clarifies that current understandings of traits do not take them to be simplistic behavioral tendencies that manifest despite contextual influences. Instead, the contemporary understanding of traits is that they are virtually always influenced by situational factors. It explores direct situational influence on action, the ways individuals influence situations, and three types of person–situation interactions. It then presents practical wisdom as a generally neglected feature of person-situation interactions. The chapter argues that practical wisdom's role in person–situation interactions goes beyond what shows up in personality research by clarifying that some individuals see more opportunities for virtue trait expression in situations than others. Moreover, this practical wisdom underwrites high-quality decision-making. It concludes by discussing how a virtue perspective adds important elements (agency, aspiration, and practical wisdom) to the contextualization of traits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".