Bundle Up Before You Go: Toward a Bundle Approach to Product Categorization
Bibliographic record
Abstract
Market participants, such as producers and audiences, often use a list of categories to label, evaluate, or promote products. Extant category research focuses overwhelmingly on a category's social properties and connectivity to explain why a category is used to describe a product. However, categories often cluster together, and little is known about how this clustering affects the appearance of a category in the description of a product. In this article, we define the easily reproducible clustering of categories as a category bundle and develop a novel measurement, bundle congruence, to measure the fitness of the category bundle. We argue that audiences employ bundle congruence to choose or exclude categories. In markets in which audiences dominate product categorization, a category's bundle congruence in a product's descriptions increases the probability that it is used for the product. Moreover, the overall bundle congruence of a product elevates the economic returns of the focal product. Our arguments are supported by an empirical analysis of feature films produced in North America. This study not only enriches the understanding of the bundle structure of the category system but also provides a novel explanation of why category spanning remains ubiquitous, despite the findings of previous studies, which assert that category-straddling products are prone to be punished financially.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".