When Categorization is Ambiguous: Factors that Facilitate and Inhibit the Use of a Multiple (Versus Single) 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 (Sujan, 1985). Very little is known, however, about the operation of this commonly studied category-based learning process under conditions of categorization ambiguity. Categorization ambiguity exists when information about a new product makes it difficult or impossible to place the novel offering in a single, existing category. Many of the new technological innovations hitting the market today fit this profile, as they often combine the features and functionality of existing products to create a single hybrid product. For example, there are personal digital assistants (PDAs) with cell phone functions and cell phones with PDA functions. The categorization of these products is highly ambiguous because, in both cases, the hybrid could logically be considered either a PDA or a cell phone.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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