Towards Critical Analysis of the Political Determinants of Health Comment on "How Neoliberalism Is Shaping the Supply of Unhealthy Commodities and What This Means for NCD Prevention"
Bibliographic record
Abstract
The recent perspective article "How Neoliberalism Is Shaping the Supply of Unhealthy Commodities and What This Means for NCD Prevention," by Lencucha and Throw, interrogates how the dominant neoliberal paradigm restricts meaningful policy action to prevent non-communicable diseases (NCDs). It contributes an NCD perspective to the existing literature on neoliberalism and health, which to date has been dominated by a focus on HIV, gender and trade agreements. It further advances the emerging commercial determinants of health (CDoH) scholarship by calling for more nuanced analysis of how the governance of both health and the economy facilitates corporate influence in policy-making. In political science terms, Lencucha and Throw are calling for greater structural analysis. However, their focus on the pragmatic, as opposed to political, aspects of neoliberalism reflects a hesitancy within health scholarship to engage in political analysis. This depoliticization of health serves neoliberal interests by delegitimizing critical questions about who sustains and benefits from current institutional norms. Lencucha and Throw's call for greater interrogation of the structures of neoliberalism forms a basis from which to advance analysis of the political determinants of health.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.084 | 0.063 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".