When Categorization Is Ambiguous: Factors That Facilitate the Use of a Multiple Category Inference Strategy
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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