Teasing Apart Overclaiming, Overconfidence, and Socially Desirable Responding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it