Bibliographic record
Abstract
Trait taxonomies such as the Big Five have been effective in studying population-level trends, but may obscure important individual differences in the structure, stability, and personal relevance of individuals’ dispositional tendencies. Inspired by Gordon Allport’s notion that people may be defined by distinctive ‘organizing foci’ of personality, this dissertation introduces the central trait approach, which focuses on identifying the most defining aspects of an individual and understanding their unique manifestation in a person’s life. Paper 1 provides proof of concept for this framework by analyzing the content and properties of open-ended central trait descriptions across 4 datasets (n=1488). Here, I test how well trait content is captured by existing nomothetic trait taxonomies and examine what properties of a trait and its relation to the rest of an individual’s personality make it “central”. Doing so, I find that although these taxonomies capture central trait content for most people, many participants nominated at least one trait that fell outside of these taxonomies, indicating the advantages of a bottom-up, open-ended method. Further, central traits broadly reflected more extreme and socially desirable aspects of individuals’ personalities. Paper 2 expands beyond self-perceptions of central traits to examine their social reality in the eyes of others. Here, I compare the content of self- and close other informant- perceptions of central traits and explore self-other agreement and multi-rater consensus of these perceptions. I find broad similarities in the content and properties of self and other nominated central traits, and that agreement about specific individuals’ traits across was modest but above chance levels. Thus, although central trait perceptions have shared reality, these perceptions diverge, likely due to idiosyncrasies what people find salient about each other. Doing so, I open up new avenues for research into interpersonal perception research and the central trait approach can mutually inform each other. Overall, this dissertation furthers current understanding of how aspects of personality become personally and socially relevant, and lays groundwork for future idiographic study 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".