Under the Umbrella: Goal-Derived Category Construction and Product Category Nesting
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
Categories are organized vertically, with product categories nested under larger umbrella categories. Meaning flows from umbrella categories to the categories beneath them, such that the construction of a new umbrella category can significantly reshape the categorical landscape. This paper explores the construction of a new umbrella category and the nesting beneath it of a product category. Specifically, we study the construction of the Quebec terroir products umbrella category and the nesting of the Quebec artisanal cheese product category under this umbrella. Our analysis shows that the construction of umbrella categories can unfold entirely separately from that of product categories and can follow a distinct categorization process. Whereas the construction of product categories may be led by entrepreneurs who make salient distinctive product attributes, the construction of umbrella categories may be led by "macro actors" removed from the market. We found that these macro actors followed a goal-derived categorization process: they first defined abstract goals and ideals for the umbrella category and only subsequently sought to populate it with product categories. Among the macro actors involved, the state played a central role in defining the meaning of the Quebec terroir category and mobilizing other macro actors into the collective project, a finding that suggests an expanded role of the state in category construction. We also found that market intermediaries are important in the nesting of product categories beneath new umbrella categories, notably by projecting identities onto producers consistent with the goals of the umbrella category. We draw on these findings to develop a process model of umbrella category construction and product category nesting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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