Evolution in Economic Geography: Institutions, Political Economy, and Adaptation
Bibliographic record
Abstract
abstract Economic geography has, over the past decade or so, drawn upon ideas from evolutionary economics in trying to understand processes of regional growth and change. Recently, some researchers have sought to delimit and develop an “evolutionary economic geography” (EEG), aiming to create a more systematic theoretical framework for research. This article provides a sympathetic critique and elaboration of this emergent EEG but takes issue with some aspects of its characterization in recent programmatic statements. While acknowledging that EEG is an evolving and pluralist project, we are concerned that the reliance on certain theoretical frameworks that are imported from evolutionary economics and complexity science threatens to isolate it from other approaches in economic geography, limiting the opportunities for cross‐fertilization. In response, the article seeks to develop a social and pluralist conception of institutions and social agency in EEG, drawing upon the writings of leading institutional economists, and to link evolutionary concepts to political economy approaches, arguing that the evolution of the economic landscape must be related to processes of capital accumulation and uneven development. As such, we favor the use of evolutionary and institutional concepts within a geographical political economy approach, rather than the construction of some kind of theoretically separate EEG—evolution in economic geography, not an evolutionary economic geography.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".