Response Styles in Affect Ratings: Making a Mountain Out of a Molehill
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
Ratings of affect words are the most commonly used method to assess pleasant affect (PA) and unpleasant affect (UA). The reliance on self-reports would be problematic if affect ratings were heavily influenced by response styles. Several recent publications have indeed suggested (a) that the influence of response styles on affect ratings is pervasive, (b) that this influence can be controlled by variations of the response format using multitrait-multimethod models, and (c) the discriminant validity of PA and UA is spurious. In this article, we examined the evidence for these claims. We demonstrate that (a) response styles have a negligible effect on affect ratings, (b) multiple response formats produce the same results as a single response format, and (c) the discriminant validity of PA and UA is not a method artifact. Rather, evidence against discriminant validity is due to the use of inappropriate response formats that respondents interpreted as bipolar scales.
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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 | 0.003 | 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