Bibliographic record
Abstract
Recent years have seen growing and coordinated interest from industry, policymakers, funders and education institutions in harnessing the potential benefits that clustering brings for growth in the UK's creative industries. By clustering, we mean the tendency of creative businesses and workers to collaborate and compete with each other in the same places. Major examples of this include the Arts and Humanities Research Council (AHRC) and UK Research and Innovation's UKRI) Creative Industries Clusters Programme, in which universities play key roles as anchor institutions within regional innovation systems, the UK government's Department for Culture, Media and Sport's (DCMS) and Creative Industries Council's Creative Industries Sector Vision, and other national creative industries strategies such as the 10x Economy vision for Northern Ireland. UK policymakers and researchers have also recently sought to investigate clusters at a wider range of geographic levels than just cities and to assess their potential for driving regional creative industries growth. Mapping studies by Nesta (Mateos-Garcia and Bakhshi, 2016 and Klinger et. al., 2018) and more recently DCMS (2022) identify creative clusters at the broad level of commuting zones. Creative PEC has shown the important role that microclusters play in the UK's creative industries, while interventions such as the Arts Council England's Cultural Development Fund have supported local cultural and creative initiatives at a neighbourhood or street level. The Local Government Association has stressed the contribution that local authorities can make to creative industries development. A growing number of local enterprise partnerships count the creative industries among their sectoral priorities. At a macro level, cities and devolved regions and nations, together with other stakeholders, are exploring whether they can increase the collective strength of their creative industries ecosystems by joining up in key areas like access to finance and skills, inspired by the experience of 'innovation corridors' in the US and Canada. The emphasis on creative clustering at different levels of geographical resolution is timely: the UK's devolutionary turn and the renewed commitment by the UK Government to 'levelling up' the economy opens up new opportunities for policy intervention and collaborative action. This first State of the Nations report from Creative PEC outlines the UK's creative industries geographies. It is the first report to explore three levels of the UK's creative industries geographies in one place: clusters, microclusters and corridors. It provides an up-to-date economic assessment of the UK's clusters and microclusters, including the impact of Covid-19, by building on the recent work commissioned by DCMS, previous reports published by Creative PEC and the earlier studies from Nesta. In addition, it presents preliminary findings from an exploratory analysis to identify creative clusters in the UK where there may be potential for developing 'creative corridors'. Those findings include a deep dive on the North of England. Our main findings are as follows: • Notwithstanding the challenges of the Covid-19 pandemic, the creative industries have grown in many parts of the UK, but significant national and regional inequalities remain. • Creative clusters grew faster than other parts of the UK before the Covid-19 pandemic, but this was not the case on average during it. However, the 55 creative clusters identified by DCMS (2022) continue to make an outsized contribution to the UK's creative industries. • Creative microclusters are the growth hotspots in the UK's creative industries, and many of these are found outside the group of creative clusters. However, microclusters outside clusters have been hit harder by the pandemic. • Based on experimental geospatial analysis, we point to broad geographic areas in the UK's nations and regions which could be further explored for their potential to become creative corridors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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; both teacher heads agree on what is shown here.
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".