Bibliographic record
Abstract
DouglassNorth's 1993 Nobel citation reads, 'for having renewed research in economic history by applying economic theory and quantitative methods in order to explain economic and institutional change'.(He shared the Prize with Robert Fogel.) Outside his field, I remember learning that he was one of the founders of cliometrics, the use of statistics in economic history.I was not as aware of his work in explaining institutional change, but that became the increasing focus of the last decades of his life.Can we take as evidence of his early interest in institutional change that, when a Berkeley undergraduate, he tells us he became a convinced Marxist?As such, he was a conscientious objector and refused to join up even after Hitler attacked Stalin on 22 June 1941 or after Pearl Harbor 7 months later.Instead, after graduation, he joined the US Merchant Marine.He was a navigator on repeated trips from San Francisco to Australia, although there is no evidence of further Australian connections.In 1942, Douglass North took a C-grade degree from University of California (UC)-Berkeley with a triple major in political science, philosophy and economics and, after the war, returned to Berkeley to write an economics dissertation on the history of life insurance in the United States.From 1952 to 1983, he taught at the University of Washington in Seattle.His research shifted from his dissertation interests to developing an analytical framework to look at regional economic growth, which led him to develop a staple theory of economic growth.After spending time with Simon Kuznets (Nobel Laureate, 1971), North undertook the empirical work that led to a major quantitative study of the US balance of payments from 1790 to 1860 -a combination of economic history and statistics that later flowered in cliometrics (Clio being the muse of history).He built on this in his first book, The Economic Growth of the United States from 1790 to 1860.From 1957 to 1960, North was one of a group of economic historians who, with support from the National Bureau of Economic Research and under the auspices of the Economic History Association, stimulated great interest in this marriage of economic history and statistical analysis.On sabbatical in Geneva in 1966-1967, North shifted his focus from the development of a single country (the US) to more than one (Europe).This immediately led him to ask the question that, by and large, occupied him for the rest of his career: why do some countries grow more slowly and less successfully than do others, despite the others' examples?In approaching this issue of economic divergence, he tells us he 'became convinced that the tools of neo-classical economic theory were not up to the task of explaining the 630871E LR0010.1177/1035304616630871TheEconomic and Labour Relations
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.200 | 0.119 |
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".