Teasing Apart Overclaiming, Overconfidence, and Socially Desirable Responding
Bibliographic record
Abstract
Contamination with positivity bias is a potential problem in virtually all areas of psychological assessment. To determine the impact of positivity bias, one common approach is to embed special indicators within one's assessment battery. Such tools range from social desirability scales to overconfidence measures to the so-called overclaiming technique. Despite the large literature on these different approaches and underlying theoretical notions, little is known about the overall nomological network-in particular, the degree to which these constructs overlap. To this end, a broad spectrum of positivity bias detection tools was administered in low-stakes settings ( N = 798) along with measures of the Big Five, grandiose narcissism, and cognitive ability. Exploratory factor analyses revealed six first-order and two second-order factors. Overclaiming was not loaded by any of the six first-order factors and overconfidence was not explained by either of the two second-order factors. All other measures were confounded with personality and/or cognitive ability. Based on our findings, overclaiming is the most distinct potential indicator of positivity bias and independent of known personality measures.
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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 | 0.032 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".