Evolution in Economic Geography: Institutions, Political Economy, and Adaptation
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
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it