Bibliographic record
Abstract
‘H.W. Arndt has been Australia’s leading scholar of Asian economic development for over thirty years’ - Former World Bank President James D Wolfensohn. \n \nThe year of Heinz Wolfgang Arndt’s birth, 1915, was not a good time for a German boy to be born. His country was soon to be defeated in a great war, his school years were shadowed by the rise of Hitler. Yet when Heinz’s long-buried Jewish background led his academic father to lose his chair in chemistry and flee to Oxford, Heinz followed. As Heinz put it, the calamity of Hitler’s rise to power led him to ‘the incredible good fortune of an Oxford education and a life spent in England and Australia.’ \n \nThis was a man of inexhaustible energy and optimism, who returned from months behind barbed wire interned in Canada to write a historical classic—The Economic Lessons of the Nineteen-Thirties. He seized the opportunity of an unexpected job offer to set off with his young family for Sydney where he quickly established himself as a leading authority on the Australian banking system, embarked on his fifty year career as a gifted university teacher and enjoyed the first of many vigorous forays as a public intellectual. \n \nBut it was at ANU that Heinz took the bold step which led him to become the Grand Old Man of Asian Economics. In 1966, just after the Sukarno coup and the year of living dangerously, he determined the time had come to study the Indonesian economy. It took all his charm, persistence and formidable intellect to persuade the Indonesians to open their doors to him. The result was a world-leading centre of Indonesian economics which greatly contributed to the development of modern Indonesia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.303 | 0.181 |
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".