Lessons from the past: International financial flows and the evolution of capital markets, Britain and Argentina, Australia, Canada, and the United States before World War I
Bibliographic record
Abstract
Introduction Economists have tended to focus their attention on short-run issues in part because the institutional structure – the rules that are observed or enforced that govern the ways in which economic agents can compete or cooperate – can be treated as exogenous and fixed. Any economist who attempts to understand the process of long-run economic growth and development, however, must immediately confront the problem of institutional change. In the long run the institutional structure does change, and the changes are at least partly endogenous. Any successful long-run analysis must explicitly include assumptions about the nature of institutional development, but we still know little about the relationship between the institutional structure and the more traditional economic variables or about the way changes in the external environment – economic, political, social, and cultural – affect the institutional structure. Much of what we do know about institutional change comes from the work of Nobel Prize winner Douglass North. To North, “the economies of scope, complementarities, and network externalities of an institutional matrix make institutional change overwhelmingly incremental and path dependent.” Since “the static nature of economic theory ill fits us to understand that process we need to construct a theoretical framework that models economic change.” Although he clearly understands the nature of the problem, we are left with a warning, an admonishment, and a number of examples.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".