Bibliographic record
Abstract
The “creative economy” creates buzz. But can it be created? Grit City (a.k.a. Tacoma, Wash.) tried to find out. The local Chamber of Commerce was the first group to work with Richard Florida, author of Rise of the Creative Class, and his Creative Class Group in an effort to grow a creative economy. The Chamber recruited 30 community members from business, government, the arts, education, and the non-profit sector to work for one year to address this issue. Florida’s Creative Class Group provided participants the background in the creative economy necessary to build on the existing economy’s strengths and form new competencies. They also provided some support as the community members developed and implemented plans to improve the creative economy. While tangible outcomes were few, the year-long experiment raised awareness about the creative class and its importance to the economy. The year also fostered connections between education, business, the arts, and the community that continue to develop as Grit City moves towards a stronger creative economy.The paper provides an overview of Florida’s creative class theory, then describes the process Tacoma used to promote a creative economy, from the initial building blocks, to the selection process of participants, to the participants’ preparation and charge, to the outcomes of the process. From this foundation, the paper explores how cities can engage people, business, government, and the non-profit sector to create a stronger creative economy.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".