DISCUSSION OF QUINN AND VOTH: “FREE FLOWS, LIMITED DIVERSIFICATION: OPENNESS AND THE FALL AND RISE OF STOCK MARKET CORRELATIONS, 1890-2001” HUW PILL
Bibliographic record
Abstract
We live in an era of financial globalisation. Yet we are not the first to do so. In an oft-quoted passage from The economic consequences of the peace – indeed, a passage that Quinn and Voth quote in the current paper – J.M. Keynes (1920) famously describes how, prior to the First World War, an investor could already “adventure his wealth in the natural resources and new enterprises of any quarter of the world … ” through a deep and integrated international capital market. By contrast, scholars of the global economy at mid-century have characterised national markets as “insular ” (McKinnon, 1981), with countries shielded from the impact of global financial developments by an extensive system of administrative controls on the cross-border flow of capital, even as international trade was progressively liberalised. What can we learn from such historical variation in capital account openness about the role of financial forces in shaping the behaviour of the global economy? How did the integrated international capital market at the turn of the nineteenth and twentieth centuries shape domestic financial institutions and markets? At mid-century, were controls on cross-border capital movements an impediment to economic and financial development? Or did they help stabilise economies subject to financial shocks?
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".