Boundaries of global health politics in the ‘fourth world’
Bibliographic record
Abstract
This chapter presents the concept of the fourth world as a useful framework for identifying and addressing health disparities that affect the most marginalised populations across societies. It discusses the rise in chronic hepatitis C virus (HCV ) infection among people who inject drugs in settings such as the United States, Canada, Australia, and the United Kingdom as a product of political, economic, social, and cultural marginalisation. The chapter highlights the importance of social mobilisation to generate political will to address HCV treatment access. It focuses on the fourth world inhabited by people who inject drugs living with HCV in so-called developed countries. The striking health disparities that require global health solutions exist within pockets of deep social and economic exclusion that exist in every society – what Manuel Castells termed the ‘fourth world’. The fourth world framework proposed by Castells is useful for understanding the politics of global health on several levels.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".