Bibliographic record
Abstract
The work of Richard Florida has proven extremely influential in cultural policy circles in recent years. His arguments concerning ‘the rise of the creative class’ and the concentration of ‘technology, talent and tolerance’ in successful cities are grounded in certain theoretical assumptions and supported by specific kinds of evidence that should be submitted to critical interrogation in order to test their robustness. This paper addresses the following questions: What are the theoretical assumptions underpinning Florida’s arguments? Is the evidence upon which these arguments are substantiated sound? What are the implications of Florida’s thesis for cultural policy? A critical reading of Florida’s key writings is presented. The paper also comments on the impact of Florida’s work around the world and focuses upon a particularly significant policy document in Britain, the Work Foundation’s Staying Ahead – The Economic Performance of the UK’s Creative Industries. It is necessary to trace the intellectual framework of ‘post‐industrial’ thinking about contemporary capitalism, the incorporation of bohemianism into business and aspirations for urban regeneration and competitive advantage in a global economy with local and regional peculiarities in order to evaluate the ‘Florida thing’. The paper reflects upon the synthesis of cultural policy with economic policy and argues that this is not the best way forward for the politics of art and culture in the twenty‐first century.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".