Placing the Creative Economy: Scale, Politics, and the Material
Bibliographic record
Abstract
Placing the creative economy: scale, politics, and the materialThe rise of the new `creative' imperative Recent interest in the role of creativity in economic development has sparked a host of conceptual and empirical studies seeking to document the rise of a creative economy, and its socioeconomic and spatial manifestations (for example, Florida, 2002;Grabher, 2001; O'Connor, 1999;Power and Scott, 2004;Pratt, 1997;Scott, 2000).Following from the work of Scott (2000), industries that are characteristic of such an economy represent a blurring of the cultural and the economic; their outputs are valued because of aesthetic rather than solely utilitarian functions.While conglomerates dominate some areas of the creative-economic landscape, creative industries are generally made up of small, agile firms that operate within a networked chain of interrelated activities.Along with creation and production, marketing and distribution are key links of this chain, critical to commodities that rely on capturing (and manipulating) consumer sensibilities (Hirsch, 1972;Pratt, 1997).The focus on creative industries in national or regional competitive strategies has been attributed to the demise of a Fordist mode of production, which was centered on cost imperatives and secured through a national, Keynesian regulatory regime.With integrated international markets and the advent of new technologies, there has been a search for new sources of competitive advantage.One critical arena for new forms of competition is an economy in which aesthetic qualities play a more prominent role and with an intensified focus on the signs and symbols of commodities (Lash and Urry, 1994).Planned obsolescence and economies of scope have become a means to fix (spatially and temporally) a crisis of overaccumulation through the marrying of the artistic with the technical and the commercial (Jameson, 1984).Indeed, a number of studies have highlighted the economic significance of creative (or `cultural') industries in late capitalism, documenting their contribution to employment, value-added production, and exports (Markusen and Schrock, 2006;Power, 2002;Pratt, 1997).Recent studies have also acknowledged that such industries tend to exhibit particular forms of socioeconomic organization, which promote innovation and experimentation.Such forms entail proximate and frequent relations among key actors along the supply chain (creators, producers, and buyers), as well as among competing actors within a particular field.A spatial concentration of such actors allows for face-to-face contact and the development of localized conventions or established `ways of doing business', including standards of compensation through `street rates'.By promoting trust and the exchange of information, these conventions help to reduce the risks of market uncertainty, making experimentation a more viable and worthy venture.Studies of the creative industries have not only privileged the `local' as the site for socioeconomic coordination but more specifically the `urban'.As a constellation of a diverse set of fields, the `urban' offers firms a range of supporting and complementary services, in addition to institutions (training, research, financial) and a significant pool of specialized workers, all of which facilitate creativity.Such place-based communities are not only a focal point for cultural labor; they are also centers of social reproduction in which cultural competencies are generated (Scott, 2000).In economic (and cultural) geography the interest in creative industries has promoted a reconsideration of the Guest editorial
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.072 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".