THE EMERGENCE OF GEOGRAPHICAL ECONOMICS: AT THE CONTESTED BOUNDARIES OF ECONOMICS, GEOGRAPHY, AND REGIONAL SCIENCE
Bibliographic record
Abstract
We explore disciplinary boundary-making in geographical economics or “the new economic geography” with attention to the approaches taken by, and attempts at communication among, scholars with primary affiliations in economics, geography, and regional science. The Dixit-Stiglitz general equilibrium approach to monopolistic competition and increasing returns was applied to agglomeration and location by Paul Krugman, who had previously pioneered the “new trade theory” building on the Dixit-Stiglitz model, and, independently and slightly earlier, by Masahisa Fujita and his student Heshem Abdel-Rahman, starting from regional science, a tradition with its own departments, doctorates, conferences, and journals distinct from economics and geography. Economic geography, as studied by geographers, had already taken a quantitative and theoretical turn in the 1960s, reviving an earlier tradition of German location theory overshadowed within geography after World War II by areal differentiation. Another strand of economic geography pursued by geographers was influenced by economic theory but by non-neoclassical Marxian and Sraffian economics. Debates between these scholars raised questions whether these analyses were multidisciplinary, drawing on distinct disciplines, or crossed disciplinary boundaries (as when geographical economics in the style of economists is undertaken in geography departments) or transcends disciplinary boundaries, or involved the emergence of a new discipline.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".