Correlations between person-descriptive items are predictable from the product of their mid-point-centered social desirability values
Bibliographic record
Abstract
This paper presents a series of pre-registered analyses testing the same theoretically derived hypothesis: If (a) the attitudes that perceivers have toward targets contribute to the variance of judgments on most items, and (b) items’ rated social desirability values align very closely with the extent to which that is the case, then the product of two items’ mid-point-centered social desirability values should predict the amount of shared variance, and thus the correlation, between these items. This hypothesis applies equally to other ratings and self-ratings. Across samples, effect sizes ranged from r = .36 to r = .80 (average r = .61) and were statistically significant in every single case. We also found that the average effect is much larger for other-ratings ( r = .71) than for self-ratings ( r = .49). This difference was also replicable and is likely rooted in the greater relative importance of the attitude factor in other-ratings, as compared to self-ratings. An exploratory item resampling analysis suggested that scales may achieve good internal consistency, and correlate substantially with other scales, based solely on shared attitude variance. We discuss the relevance of these findings across different domains of psychological assessment, and possible ways of dealing with the issue.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".