Coherence within and across Categories: The Dynamic Viability of Product Categories on Kickstarter
Bibliographic record
Abstract
How does the viability of a product category shift over time? Studies abound on how categories emerge and become established, or fall out of use. Yet, extant research has often examined the evolution of categories one at a time, leaving open the question of how related categories affect a focal category’s viability. In contrast, we consider both intra- and inter-category dynamics. Viewing categories as continuously shaped by actors’ efforts to position their products, we argue that these efforts alter the coherence that products exhibit not only within a category (a category’s heterogeneity), but also across related categories (a category’s distinctiveness). We theorize how the interaction between a category’s heterogeneity and distinctiveness shapes its subsequent viability. When a focal category’s distinctiveness is low, the heterogeneity–viability relationship takes an inverted U-shape. However, as distinctiveness grows, the relationship flattens and eventually flips to a U-shape. We explain this by considering the trade-off between the “classification” and “valuation” benefits that a category affords. We find support for our argument by tracking 170 categories over an 11-year period on Kickstarter, one of the largest crowdfunding platforms. By providing a nuanced understanding of category dynamics, we shed new light onto the fluctuating viability of categories.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".