Geographies of scope: an empirical analysis of entertainment, 1970-2000
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The geographic clustering of economic activity has long been understood in terms of economies of scale across space. This paper introduces the construct of geographies of scope, which we argue is driven by substantial, large-scale geographic concentrations of related skills, inputs and capabilities. We examine this through an empirical analysis of the entertainment industry across US metropolitan areas from 1970 to 2000. Our findings indicate that geographies of scope (or collocation among key related entertainment subsectors and inputs) explain much of the economic geography of entertainment even when scale is controlled for, though our regressions over time suggest the role of scope is decreasing. Furthermore, we find that the entertainment sector as a whole and its key subsectors are significantly concentrated in two superstar cities—New York and Los Angeles—far beyond what their population size (or scale effects) can account for, while the pattern falls off dramatically for other large regions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it