Openness to (reporting) experiences that one never had: Overclaiming as an outcome of the knowledge accumulated through a proclivity for cognitive and aesthetic exploration.
Bibliographic record
Abstract
Overclaiming-in which individuals overstate their level of familiarity with items-has been proposed as a potential indicator of positive self-presentation. However, the precise nature and determinants of overclaiming are not well understood. Herein, we provide novel insights into overclaiming through 4 primary studies (comprising 6 samples) and a meta-analysis. Based on past empirical work and theoretical discussions suggesting that overclaiming may be the result of several processes-including an egoistic tendency to self-enhance, intentional impression managing behavior, and memory biases-we investigate various potential dispositional bases of this behavior. We hypothesized that overclaiming would best be predicted by a dispositional tendency to be curious and explorative (i.e., high Openness to Experience) and by a dispositional tendency to be disingenuous and self-centered (i.e., low Honesty-Humility). All studies provided support for the first hypothesis; that is, overclaiming was positively associated with Openness. However, no study supported the hypothesis that overclaiming was associated with Honesty-Humility. The third and fourth studies, where multiple mechanisms were compared simultaneously, further revealed that overclaiming can be understood as a result of knowledge accumulated through a general proclivity for cognitive and aesthetic exploration (i.e., Openness) and, to a lesser extent, time spent in formal education. (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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".