To Be or Not to Be: Exploring the Nature of Positively and Negatively Keyed Personality Items in High-Stakes Testing
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
Including equal numbers of positively and negatively keyed items is common in Five-Factor Model (FFM) personality measures. Much literature has demonstrated the presence of positive and negative keying factors in low-stakes testing situations, but there is a dearth of research investigating these factors in high-stakes testing. To address this gap, we investigated whether an FFM measure used in high-stakes testing was influenced by positive and negative keying factors. We also examined the overlap of the positive and negative keying factors with social desirability, rule-consciousness, acquiescence, and cognitive ability. Confirmatory factor analysis supported the inclusion of distinct factors associated with positively and negatively keyed items and suggested that the keying factors accounted for a substantial portion of variation in responses to FFM items. Social desirability and rule-consciousness were found to have significant relations with both keying factors, whereas acquiescence was only related to the negative keying factor. Implications for the construct validity of FFM measures used in high-stakes testing and directions for future research are discussed.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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