Case Studies in Cultural Entrepreneurship
Bibliographic record
Abstract
This book of five case studies demonstrates the critical role entrepreneurs and entrepreneurial thinking play in reinventing cultural organizations to make them relevant and sustainable for the twenty-first century and beyond. Through the twin lenses of cultural entrepreneurship and organizational change, these readable and inspirational cases offer an in-depth analysis of how a variety of cultural organizations—small and large; local, regional and national; museums and arts organizations—have found opportunities in complex situations to create new identities and missions and, in doing so, have revitalized their organizations and in many cases, surrounding communities. Cases include: The Strong: how a museum in Rochester, New York, forged an entirely new national identity as The National Museum of Play. National Mississippi River Museum and Aquarium: how the Mississippi River Museum developed and nurtured a network of partnerships to create a new regional identity and, in doing so, revitalized the waterfront area of Dubuque, Iowa. Montreal Center for History: using oral history and community collaborations to dramatically build its audiences throughout the city. Proctors: how an arts organization revitalized downtown Schenectady, New York Weeksville: how an institution in one of the poorest neighborhoods in New York City found a niche that provided vital services to its constituency.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".