Bibliographic record
Abstract
Jeffrey G. Williamson of Harvard University and University of Wisconsin reviews “Economic Development in the Americas since 1500: Endowments and Institutions” by Stanley L. Engerman and Kenneth L. Sokoloff. The EconLit abstract of the reviewed work begins: Eleven papers explore differences in the rates of economic growth in Latin America and mainland North America, specifically the United States and Canada, and consider how relative differences in growth over time are related to differences in the institutions that developed in different economies. Papers discuss paths of development -- an overview; factor endowments and institutions; the role of institutions in shaping factor endowments; the evolution of suffrage institutions; the evolution of schooling – 1800–1925; inequality and the evolution of taxation; land and immigration policies; politics and banking systems; five hundred years of European colonization; institutional and noninstitutional explanations of economic development; and institutions in political and economic development. Engerman is John H. Munro Professor of Economics and Professor of History at the University of Rochester. The late Sokoloff was Professor in the Department of Economics at the University of California, Los Angeles. Bibliography; index.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.352 | 0.323 |
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".