Globalisation and neoliberalism as structural drivers of health inequities
Bibliographic record
Abstract
In this paper, we draw upon and build on three presentations which were part of the plenary session on 'Structural Drivers of Health Inequities' at the National Conference on Health Inequities in India: Transformative Research for Action, organised by the Achutha Menon Centre for Health Science Studies in Trivandrum, India. The three presentations discussed the influential role played by globalisation and neoliberalism in shaping economic, social and political relationships across developed and developing countries. The paper further argues that the twin process of globalisation and liberalisation have been important drivers of health inequities. The first segment of the paper attempts a broader conceptualisation of neoliberalism beyond the economic realm. Using Stephanie Lee Mudge's conceptualisation (Soc Econ Rev 6:703-3, 2008) we have analysed how the political, bureaucratic and intellectual domains of neoliberalism have intersected and redefined the role of state and commercialised health services leading to inequities. Neoliberal ideas have reconfigured the role and changed the priorities of non-governmental organisations resulting in a fracture within this movement. n the second segment, we focus on the rise of American philanthro-capitalism, and how the two major foundations, the Rockefeller Foundation (early twentieth century) and the Bill and Melinda Gates Foundation (twenty-first century), have shaped the ideology of institutions engaged in international health and influenced the global health agenda. We discuss how the activities of philanthro-capitalists have transformed the architecture of health governance through their top-down organisational culture and deficit of structures to ensure accountability. The third and final segment of the paper focuses on how neoliberalism as a political project and cultural movement has forged alliances with conservative politics and religious fundamentalisms, resulting in negative consequences for women and other marginalised groups. These alliances have resulted in the control of women's bodies and contributed to the reversal of hard-won rights for health and gender justice in many parts of the world.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".