Experimental Approaches to Individual Differences and Change: Exploring the Causes and Consequences of Extraversion
Bibliographic record
Abstract
Abstract Positive psychologists, like most other people, wish to better understand happiness. While our environments and efforts surely play roles, it is also clear that some people are dispositionally more (or less) prone to happiness. For example, the personality traits of extraversion and neuroticism are among the best predictors of happiness (negatively in the case of neuroticism; Diener & Seligman, 2002; Diener, Suh, Lucas, & Smith, 1999). Nonetheless, naming these traits does not tell us what causes individual differences in happiness. The question remains: What are the processes that underlie the traits of extraversion and neuroticism, or individual differences in happiness? That is, what, specifically, are the stable aspects of personality (e.g., genes, beliefs, etc.) that contribute to behavior and experience across situations, and how do they manifest themselves, in conjunction with changing situations, over time? For example, do extraverts put themselves into more positive environments, or do they respond more positively than introverts while sharing roughly equivalent environments? In either case, how, at the level of psychological processes, is this accomplished (e.g., differences in attentional biases or emotional responsiveness)? Understanding the causal processes underlying individual differences provides an attractive goal for research, but also raises significant challenges. Most obviously, individual differences (like extraversion or happiness) are not easily manipulated and, as every psychologist learned in his or her first methods course, manipulation is the key to establishing causality in psychology.
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.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".