Strategic Categorization, Category Bundle, and Typecasting: Three Essays on Product Categorization
Bibliographic record
Abstract
Categories are social agreements about the meanings of labels applied to products. Categories serve as the basis for market interaction: audiences use categories to make sense of the products offered to them, producers apply product categories in marketing activities to reach their target customers, and market intermediaries refer to prototypical categories in assessing the quality of products. As a widely used sociocognitive concept, categorization has accrued prominence in research and practice, with researchers investigating the social and economic impacts of categorization and practitioners probing superior categorization strategies that optimize their economic returns. However, current strategic management and organization theory research has achieved limited success in expounding on how organizations strategically manipulate category labels to acquire excess returns and how audiences process categorical information in assessing the products to which they are exposed. This dissertation joins the ongoing dialogue on categorization and contributes to the literature by offering three essays that respectively address three understudied questions. First, how do producers manipulate the categorical perception of the audiences for their offerings? Second, how do audiences handle the interconnected relationships between categories when they classify products in the market? Last, how do the market identities imposed on market candidates persistently affect their career development? I chose the feature film industry in North America (Canada and the U.S.) as the empirical setting for my dissertation, since a dominant category system, film genres, significantly affects the market success of all film market participants. The genre labels associated with a film shape moviegoers consumption decisions, and the categorical perception of moviegoers of an actor/actress has considerable impacts on the actors/actresss career advancement. Using a gigantic database of feature film projects that were exhibited in theaters in the U.S. and Canada from 1990 to 2015, I construct three unique datasets that are respectively used to test my hypotheses and answer my research questions at the film, genre, and actor levels. I summarize my key findings as follows. This dissertation contributes to category, labor market, and strategic management research.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".