Globalization and social determinants of health: The role of the global marketplace (part 2 of 3)
Bibliographic record
Abstract
Globalization is a key context for the study of social determinants of health (SDH): broadly stated, SDH are the conditions in which people live and work, and that affect their opportunities to lead healthy lives. In the first article in this three part series, we described the origins of the series in work conducted for the Globalization Knowledge Network of the World Health Organization's Commission on Social Determinants of Health and in the Commission's specific concern with health equity. We identified and defended a definition of globalization that gives primacy to the drivers and effects of transnational economic integration, and addressed a number of important conceptual and methodological issues in studying globalization's effects on SDH and their distribution, emphasizing the need for transdisciplinary approaches that reflect the complexity of the topic. In this second article, we identify and describe several, often interacting clusters of pathways leading from globalization to changes in SDH that are relevant to health equity. These involve: trade liberalization; the global reorganization of production and labour markets; debt crises and economic restructuring; financial liberalization; urban settings; influences that operate by way of the physical environment; and health systems changed by the global marketplace.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".