Book review of: C. Dyble, Taming Leviathan: Waging a War of Ideas Around the World
Bibliographic record
Abstract
This essay is my review of Colleen Dyble’s book, Taming Leviathan: Waging a War of Ideas around the World. Dyble is affiliated with the legendary classical liberal British think tank, the Institute of Economic Affairs. Her anthology is a collection of essays by people around the world who have been involved with similar free-market think tanks in countries with historically statist economic systems. These writers include Greg Lindsay, founder of the Center for Independent Studies in Australia; Margaret Tse, of the Instituto Liberdade in Brazil; Michael Walker, co-founder of the Fraser Institute in Canada; Cristian Larroulet, of Libertad y Desarrollo in Chile; Giancarlo Ibarguen, of the Center for Economic and Social Studies in Guatemala (which actually founded a free-market-oriented university); Parth Shah, founder of the Center for Civil Society in India; Daniel Doron, co-founder of the Israeli Center for Social and Economic Progress; Alberto Mingardi, of the Instituto Bruno Leoni in Italy; Masaru Uchigama, founder of the Japanese for Tax Reform; Elena Leontjeva, co-founder of the Lithuanian Free Market Institute; Alexander Magno, co-founder of the Foundation for Economic Freedom in the Philippines; and last, Leon Louw, of the Free Market Foundation of South Africa. I then discuss seven important reasons why classical liberal think tanks are so important in modern societies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.042 |
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".