Fashioning the boutique location: Remaking the Gold Coast as an international production location
Bibliographic record
Abstract
Over the 1990s and 2000s, the number of places and personnel involved in peripatetic international film and television production expanded considerably to include not just the obvious global media city candidates with substantial ongoing film and television infrastructure such as a London, New York or Rome, or the middle-ranking media cities like Miami, Sydney, Toronto or Prague, but also new production locations with little or no previous experience of continuous film and television production. Notable examples of these greenfield developments are Wilmington in North Carolina; Vancouver; Bucharest and Gold Coast, Australia. Furthermore, with the dramatic expansion in the range and variety of places involved in international film and television production, the rules of the game in peripatetic international production were also changing. In the late 1980s and early 1990s the locations market represented a handful of participating places. By the 2000s it had become global and crowded, representing a plethora of location interests vying for a share of global production capital, with places promoting their various advantages-from financial incentives to available natural and built environments, from the comfort of lead actors and personnel to the security of competence and expertise-as enticements to secure film and television production. Copyright © 2010 by Lexington Books
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".