Global inequality and human needs : health and illness in an increasingly unequal world
Bibliographic record
Abstract
1. Thinking Socially About Health. Thinking Socially About Health. Social Networks, Social Isolation, and Health. Thinking Socially About Health: The AIDS Epidemic. Poverty, Social Inequality and Health. How Does Social Inequality Bring About Differences in Health? Inequality Internationally. The Political Economy of Health: Adding Global and Power Dimensions toSocial Influences on Health. How this Book is Organized. 2. Theoretical Foundations for Studying Inequalities. Explaining Variations in Population Health. Foundations for a Social Science of and Inequality. The Two Transitions of Developed Countries. Labor Force Relationships. Theoretical Foundations for Understanding Gender Differences in Health. Social Hierarchy's Effects on Mind and Body. Social Cohesion and Income Inequality. Summary. 3. A Political Economy Approach to Health. The World Economic System. A Global Model of Social Influences on Health. 4. and Inequality: Principles and Examples. Principle 1. Living Conditions and Human Needs. Principle 2. Affluence and Life Expectancy. Principle 3. Improvements in Standard of Living and Health. Principle 4. Social Inequality and Variations in Health. Principle 5. Direct and Indirect Effects of Social Inequality on Health. Principle 6. Social Class' Association with Outcomes. The Russian Case Example. Policies Matter! 5. Thinking Globally About Health. Global Trends. Regional Trends in World Health. Africa. Latin America and the Caribbean. Canada. The United States. The Eastern Mediterranean Region. Europe. The Western Pacific. China. South East Asia. India. Conclusion. 6. Gender, Race and Ethnic Influences on Health. Gender and Health. Women's and Poverty. Gender and in the U.S. Women's Status and Health: A Study of the United States. Principle 7. Gender Equality and Outcomes. A Global Approach to Racial and Ethnic Differences in Health. Principle 8. Ethnicity, Race and Outcomes. Differences among Racial and Ethnic Groups in the United States. Conclusion. 7. Mental Health: Social and Global Issues. Major Categories of Mental Illness. A Cross-National Study of Mental Illness: Clues to the Global Prevalence and Correlates of Mental Illness. Poverty, Unemployment and Job Insecurity. Social Support and Mental Health. Alienation and Social Exclusion. Homelessness and Mental Health. A Global Perspective on Mental Health. Cultural Variations, Economic Development and Mental Health. Mental Trends. Initiatives To Improve Global Mental Health. 8. Underdevelopment and Health. Underdevelopment and Health. Characteristics of Countries in the World Economic System. Inequality and Human Needs. and Life Expectancy in the Periphery. Causes of Underdevelopment and Poor Health: African Countries. Case Examples. The Policies of the World Bank and the International Monetary Fund. A Reformed World Bank? Programs That Improve Quality of Life and for the Poor. Prospects for Improvement? 9. Development and Health: Promise and Limitations. Approaches to Development. Refining the Wealth Equals Health Formula: Six Dynamics. A Country's Readiness for Development. Principle 9. Development, Inequality and Outcomes. Internal Development Processes. The Extent To Which The Majority Benefits From Development. The Extent To Which Inequality Increases With Development. The Effectiveness Of Civic-Minded State Policies. The Epidemiology of the Country. Recipe for Healthy Development: Mix in Social Welfare and Equality-Enhancing Policies. 10. Policies for Building Healthy Societies. Policies Matter! Tackling HIV/AIDS. Making Societies Healthier. National and Global Policies for Healthier Societies. What Can We Do as Individuals?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".