EDUCATION, GENDER EQUALITY, SOCIAL WELL-BEING AND ECONOMIC DEVELOPMENT IN AMERICAN COUNTRIES, 2000-2010
Bibliographic record
Abstract
We analyse the important role of education in economic development and social wellbeing of American countries, including indicators of gender opportunities for development as part of social well-being. In this regard we select some indicators which usually have a great importance for reaching improvements in social well-being such as Government effectiveness and voice of citizens, among Governance Indicators, and the indicator of interpersonal trust from World Values Survey. Regarding life satisfaction we analyse the correlations of three indexes with economic development and other variables. The USA and Canada have a clear outstanding position in average educational indicators and other variables, while only a few Latin American and Caribbean countries show values of education spending above World average. In spite of the UN declaration of the Millennium Development Goals (MDGs) many American countries show very low levels of education spending for the period 2000-2007. Fostering international cooperation to finance education is of uppermost importance to achieve the MDGs in those cases, and to guarantee socio-economic development. Regarding economic and social equality of opportunities for Women we find also a positive impact of education. Finally we present some econometric models which relate life satisfaction with economic development, gender equality and other variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".