International comparisons of world development: 2 ebooks on education, production, poverty and quality of life for the periods 1960-2000 and 2021-2023
Bibliographic record
Abstract
In Section 1 we cite a paper with some selected readings on international cooperation to development written for the first quarter of the 21st century, and the role of academic Blogs on social diffusion of those studies. In sections 2 and 3 we present a reference to 2 ebooks published by the Euro-American Assocition of Economic Development Studies, in year 2003. Section 2 summarizes some of the main contents of the Book EE11, by Guisan, Aguayo and Exposito(2023), on World development for 1960-2000, and Section 3 summarizes the main contents of the book by Guisan(2023), EE12, for the period 2001-2023. Both books include results of international econometric models that relate Education, Production per capita and Indicators of Quality of Life, with data from the OECD, Latin America, Africa, Asia or other areas. Both books are free downloadable and include links to more than 150 interesting studies cited in the bibliography, many of them free available. The main conclusion from the empirical studies is that international cooperation should be focused on support to education, production, investment, peace, and quality of government.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.041 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".