Bibliographic record
Abstract
Which shows that the story in this book is a history of humankind, told from the perspective of gamblers, risk-takers, and those who finance them. It tells the story of people who leap into the unknown, hoping to leapfrog their fellows. Since ancient times people have tried to understand what makes some places rich and others poor. Herodotus wrote 2,500 years ago in his Histories : For most cities which were great once are small today; and those which used to be small were great in my own time. Knowing, therefore, that human prosperity never abides long in some place, I shall pay attention to both alike. Over the past few decades, countries like Ireland, Singapore, Taiwan, and China have leapfrogged others, which were better endowed with natural resources and whose populations had more formal education. At the same time, many oil-producing countries, for example, despite the windfall of money from selling oil, have not succeeded in leveraging their riches into prosperity. Risk and financial markets play relatively marginal roles – if any – in most views that examine the previous question, and though gamblers and risk-takers occasionally do make their appearance, the link between what is “civilization” on one side and risk-taking, creating liquidity, and financial markets on the other is rarely made.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".