It Is Not the Whole Story: Toward a Broader Understanding of Entrepreneurial Ventures’ Symbolic Differentiation
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
Entrepreneurial ventures strategically communicate information about themselves to convey their distinctiveness and attract favorable audience attention. This study explores how the possession of quality-signaling resources, such as patents, influences the degree to which entrepreneurial ventures convey distinctiveness in their entrepreneurial narratives. Cultural entrepreneurship research spotlights such resources as the ingredients around which entrepreneurs construct distinctive narratives and proposes that resource-rich ventures will present themselves as particularly distinctive. Challenging this, we argue that ventures rich in quality-signaling resources—while ideally positioned to convey their distinctiveness—will likely forgo this symbolic differentiation opportunity under certain industry conditions due to a lack of external incentives. Our analysis of 31,270 UK-based ventures launched between 2010 and 2021 finds that, compared to patent-poor ventures, patent-rich ventures exhibit higher levels of narrative distinctiveness when situated in industries that receive little attention, but substantially lower levels of narrative distinctiveness when situated in hot industries that attract a lot of attention. In doing so, our study challenges the assumption that entrepreneurial ventures always aim to present themselves as distinctive as legitimately possible, delineates conditions under which this assumption is likely violated, and lays the groundwork for a broader research agenda on organizations’ substantive and symbolic differentiation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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