Worse is Bad: Divergent Inferences From Logically Equivalent Comparisons
Bibliographic record
Abstract
Comparisons can often be framed in different but equivalent ways. For example, “A is better than B,” can also be expressed as “B is worse than A.” In seven studies (and four further studies in the Supplemental Materials) we find that logically-equivalent comparison frames have divergent effects on judgments and choices for the items being compared as well as other members of the set from which those items were drawn. These effects are asymmetric, affecting inferior items more strongly than superior ones. We propose a “comparison framing” account that draws on theory in linguistics on the “markedness” of adjectives (Cruse, 1976; Lehrer, 1985) to explain these results. We show that this account fits the data better than two previous accounts of attribute framing: automatic valenced associations (Levin, Schneider, & Gaeth, 1998) and leakage of information (McKenzie & Nelson, 2003).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".