When Categorization Is Ambiguous: Factors That Facilitate the Use of a Multiple Category Inference Strategy
Bibliographic record
Abstract
Prior research has established that categorization plays a central role in new product learning. Very little is known, however, about category‐based learning under conditions of categorization ambiguity. Of particular interest is whether and under what circumstances consumers might employ a multiple‐ (vs. single‐) category strategy to generate inferences about ambiguous products. In this research, we identified 2 factors—category familiarity and the nature of the category cue—that are responsible for determining whether inferences are based on a single category or multiple, competing categories. The results of 3 studies suggest that when an ambiguous product is described in terms of conflicting conceptual and perceptual category cues, a single category inference strategy is employed when the perceptually cued category is more familiar than the conceptually cued category. In particular, inferences are based largely on the perceptually cued category under these circumstances. However, when the perceptually cued category is less than or equal to the conceptually cued category in familiarity, a multiple category inference strategy is employed and inferences are based on both the perceptually and conceptually cued categories.
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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 | 0.007 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".