Creative Economy Employment in the US, Canada and the UK
Bibliographic record
Abstract
The US has the largest creative economy employment of the US, UK and Canada employing 14.2 million people. \n \nCanada had the largest creative economy employment as a percentage of the workforce at 12.9 per cent. \n \nEmployment in the UK creative economy grew at 4.7 per cent per annum on average between 2011 and 2013, faster than the US between (3.1 per cent). A comparison with Canadian growth over this period was not possible with the data available. \n \nThe largest centre of creative economy employment in the US in absolute terms is the New York-Newark-New Jersey Metro area employing 1.2 million people (12.7 per cent of the workforce) in 2013. \n \nThe creative economy employment of this area in absolute and percentage terms is comparable to that of the Greater South East of England (London, the South East and Eastern regions). This employs 1.3 million people in the creative economy, 12.3 per cent of the workforce. \n \nThis report provides consistent statistics on the US and Canadian creative economies in comparison to the UK. Creative economy employment being employment in creative industries and in creative occupations outside of these. Employment figures for creative industry groups are also provided. \n \nThe report also analyses creative economy employment at a sub-national level for the US and UK, and the national level growth rates for these two countries between 2011 and 2013. \n \nThe report applies the official UK creative industry classification to produce a best possible fit creative industries definition in the US and Canadian data. The report is based on analysing the US American Community Survey, the Canadian Household survey and the UK Annual Population Survey. \n \n A companion report that examines the creative industries employment in the 28 member states of the EU was published in December 2015.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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".