Why Do People Agree With Both Regular and Reversed Items? A Logical Response Perspective
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
The current research challenges the received view that misresponses to reversed items are the result of participants' irrational behavior. On the contrary, for participants at a midpoint level of a trait, it is perfectly logical to agree and disagree with seemingly equivalent statements (e.g., I'm not tall, but neither am I short). For this reason, regular and reversed items for a unidimensional construct were predicted to load on separate factors. Two types of reversed items-polar opposites and negated regular items-were also predicted to load on separate factors, as they are qualitatively different. An empirical study supported this explanation. Differential responding was found to be highest for participants at the mid-level of a trait and diminished toward the ends, revealing a quadratic relationship and a trait × method interaction. Thus, our findings demonstrate that the logical response pattern of individuals at the mid-level of a trait is another previously unrecognized explanation for why the inclusion of regular and reverse-keyed items can contribute to the apparent bi-dimensionality of unidimensional constructs (for data analysis results, see https://osf.io/krh2w/).
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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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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