Categories and narratives as sources of distinctiveness: Cultural entrepreneurship within and across categories
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
Abstract Research Summary Cultural entrepreneurship theory suggests that entrepreneurial narratives need to be optimally distinctive—neither portraying an offering as too similar to nor too distinctive from the conventions of its product category—for attracting superior demand. Building on and extending this literature, we propose that the benefits and downsides of a distinctive narrative fundamentally depend on a category's distinctiveness vis‐à‐vis alternative categories because distinctive categories (a) provide an important source of differentiation for their members and (b) disproportionally attract those audience members that highly value novelty. Our longitudinal study of 159,343 Airbnb listings in 45 categories strongly supports our hypotheses: the relationship between Airbnb listings' narrative distinctiveness and demand‐side performance flips from an inverted U‐shaped curve in indistinctive categories to a U‐shaped curve in distinctive categories. Managerial Summary Entrepreneurs need to craft a compelling narrative around their offering to legitimate and differentiate it from competing offerings. In this article, we explore when and why entrepreneurs should craft narratives that portray their offerings as similar, moderately distinctive, or highly distinctive from other offerings. We study this question in the context of the Airbnb marketplace, in which Airbnb hosts compete with their respective accommodation listings. Our study shows that Airbnb listings in indistinctive categories attract most demand when their narratives portray them as moderately distinctive. In contrast, Airbnb listings in distinctive categories attract the most demand when their narratives portray them as either highly similar or highly distinctive from other listings in their category
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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