Bibliographic record
Abstract
James Heilbrun and Charles M. Gray, The Economics of Art and Culture, 2nd edition. New York: Cambridge University Press, 2001, 410 pp. Sociological analysis of the arts has developed over the last 35 years as arts organizations increased enormously in number and in their degree of institutionalization. So too has public support for the arts and other cultural expressions, in North America through the Canada Council and the National Endowment for the Arts, provincial, state and local agencies. Increasing in turn has been the need for such organizations to become publicly accountable, thereby providing systematic and quantitative data subject to more than the ethnographic and case study analysis by social scientists that have formed the original basis of the field. Such studies continue to be done, but now greatly enhanced by the more universal survey data that is available. Not surprisingly, as the field has grown so too have academic conferences and publications proliferated. Most notable for sociologists is the annual Conference on Social Theory, Politics and the Arts and its now-affiliated journal, the Journal of Arts Management and Law. The STPA in 1989 our host at University of Toronto was Joseph G. Green and in 1996 at University of Montreal, Francois Colbert organized the conference. Each published selected proceedings under the respective titles of From Orchestras to Apartheid (Guiot & Green, 1990; it includes the keynote address by Mayor Moore) and Selected Proceedings: Cultural Policy (Colbert 1997). While STP&A has been multi-disciplinary since its early years, including sociologists, political scientists and policy analysts, anthropologists, historians, aestheticians, economists, arts administrators and occasionally even an artist (!), the Conference on Cultural Economics has attracted primarily economists, drawn to mathematical models. It has produced its own eponymous journal. There are now European-based variants of both of the above conferences and journals, to say nothing of mainstream publication of anthologies and other studies in the field, with many social scientist multi-linguists participating in both. Notable among these has been cultural economist Harry Hillman Chartrand of Saskatoon, most of whose publications are available online. With all of this scholarly attention, what have we learned? The book under discussion here, The Economics of Art and Culture (2nd edition), is gracefully written, filled with more recent data and sound analysis. James Heilbrun and Charles M. Gray are both economists, if somewhat less inclusive in their purview than Chartrand in their analysis of the culture industry. (Indeed, they do not cite his work.) The first edition of their text was published in 1993. Their second edition, like the first, focuses primarily on the micro- but also the macroeconomics of artistic production and its social meanings. They provide a remarkably clear analysis of a large number of data sets, summarized in tables that even someone like myself, qualitative to the core, can understand, along with a few mathematical models. The data are taken from sources in the United States and internationally, public agencies as well as foundation and corporate reports. Their analysis is nuanced in recognition of the limitations of such surve y quantitative data. They are consistent in comparing the views of those who oppose any public funding of the arts, who favor complete privatization, with those who support at least some such funding -- as do they. …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.060 | 0.031 |
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".