Entrepreneurial Visions as Rhetorical History: A Diegetic Narrative Model of Stakeholder Enrollment
Bibliographic record
Abstract
Research suggests that entrepreneurs persuade stakeholders to engage in risky projects in an uncertain future through visions, compelling narratives of the future. A unique challenge for entrepreneurs, however, is how entrepreneurs can construct a narrative that unites stakeholders with different perceptions of the degree of risk or uncertainty posed by the future. We address this question with a diegetic narrative model of stakeholder enrollment. Our primary argument is that to reduce variation in how potential stakeholders view the future, a story must embed a vision of the future in a coherent and collectively held narrative of the past. We introduce rhetorical history as the primary construct through which this occurs. We demonstrate how successful visions employ historical tropes at the intradiegetic level to appeal to individual perceptions of risk or uncertainty and how those historical tropes are combined into meta-narratives or myths drawn from the collective memory of a community to create broad, extradiegetic appeal to all stakeholders regardless of their temporal orientation. Finally we describe three categories of historical reasoning – teleological, presentism, and retro-futurism – that act as bridging mechanisms between past, present and future that provides stakeholders with an enhance sense of agency in the future.
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.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".