The Oxford Handbook of Creative Industries
Bibliographic record
Abstract
The Oxford Handbook of Creative Industries is a reference work, bringing together many of the world's leading scholars in the application of creativity in economics, business and management, law, policy studies, organization studies and psychology. Creative industries research has become a regular theme in academic journals and conferences across these subjects and is also an important agenda for governments throughout the world, while business people from established companies and entrepreneurs revaluate and innovate their models in creative industries. The Handbook is organized into four parts: Following the editors' introduction, Part One on Creativity includes individual creativity and how this scales up to teams, social networks, cities, and labour markets. Part Two addresses Generating and Appropriating Value from Creativity, as achieved by agents and organizations, such as entrepreneurs, stars and markets for symbolic goods, and considers how performance is measured in the creative industries. Part Three covers the mechanics of Managing and Organizing Creative Industries, with chapters on the role of brokerage and mediation in creative industry networks, disintermediation and glocalisation due to digital technology, the management of project-based organzations in creative industries, organizing events in creative fields, project ecologies, Global Production Networks, genres and classification and sunk costs and dynamics of creative industries. Part Four on Creative Industries, Culture and the Economy offers chapters on cultural change and entrepreneurship, on development, on copyright, economic spillovers and government policy. This authoritative collection is the most comprehensive source of the state of knowledge in the increasingly important field of creative industries research. Covering emerging economies and new technologies, it will be of interest to scholars and students of the arts, business, innovation, and policy. Contributors to this volume - Sonos Abdel-Gawad, ESADE Business School Patrick Adler, University of California Los Angeles Narasimhan Anand, IMD Business School Hasan Bakhshi, Queensland University of Technology Gerben Bakker, London School of Economics and Political Science Gino Cattani, Stern School of Business Neil Coe, National University of Singapore Mariachiara Colucci, University of Bologna Gregoire Croidieu, Grenoble Ecole de Management Stuart Cunningham, Queensland University of Technology Elizabeth Currid, University of Southern California Robert DeFillippi, Suffolk University Anna Dempster, Sotheby's Institute of Art Simone Ferrani, University of Bologna Vladi Finotto, Ca' Foscari University Richard Florida, University of Toronto Stephen Flowers, University of Brighton Pacey Foster, University of Massachusetts Lucy Gilson, University of Connecticut Daniel Gruber, Medill School of Journalism Elizabeth Gulledge, The University of St Andrews Allegre Hadida, University of Cambridge Paul Hirsch, Northwestern University Candace Jones, Boston College James Kaufman, California State University Sean Keddy, University of Waterloo Mukti Khaire, Harvard Business School Martin Kretschmer, University of Glascow Mark Lorenzen, Copenhagen Business School Fiona Macmillan, University of London Elonora di Maria, University of Padova Juan Mateos-Garcia, Nesta Charlotta Mellander, Jonkoping International Business School Pierre-Michel Menger, Centre National de la Recherche Scientifique Richard Ocejo, John Jay College of Criminal Justice Jason Potts, RMIT University Andy Pratt, King's College London Francesco Rullani, LUISS Guido Carli Jonathan Sapsed, University of Brighton Elke Schussler, Freie Universitat Berlin Barnara Slawich, IESEG School of Management Robert Sternberg, Oklahoma State University Silviya Svejenova, Copenhagen Business School Jorg Sydow, Freie Universitat Berlin Barbara Townley, University of St Andrews Tara Vinodrai, University of Waterloo Georgina Voss, University of Brighton
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.116 | 0.055 |
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".