When Two Wrongs Make a Right: Using Conjunctive Enablers to Enhance Evaluations for Extremely Incongruent New Products
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
Abstract The success of new incongruent products hinges largely on whether consumers can efficiently make sense of the product. One of the most efficient ways that people make sense of new objects is through feature-based association. Such associations often incorporate an enabler (e.g., the color green) to help make sense of a semantically related feature (e.g., vitamin enriched). Evidence from three studies suggests that marketers can strategically incorporate enablers in product design to help consumers make sense of an extremely incongruent feature. As a result, consumers tend to reflect more favorably on the product. Furthermore, the authors find that even if the enabler itself is incongruent and leads to lower evaluations on its own, when combined with an atypical feature the effect can still be positive. Thus, a small but semantically meaningful adjustment in design can help marketers successfully introduce extremely incongruent innovations.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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