The Ghost Is the Machine: How Can We Visibilize the Unseen Norms and Power of Global Health?; Comment on 'Navigating between Stealth Advocacy and Unconscious Dogmatism: The Challenge of Researching the Norms, Politics and Power of Global Health'
Bibliographic record
Abstract
In his recent commentary, Gorik Ooms argues that “denying that researchers, like all humans, have personal opinions … drives researchers’ personal opinion underground, turning global health science into unconscious dogmatism or stealth advocacy, avoiding the crucial debate about the politics and underlying normative premises of global health.” These ‘unconscious’ dimensions of global health are as Ooms and others suggest, rooted in its unacknowledged normative, political and power aspects. But why would these aspects be either unconscious or unacknowledged? In this commentary, I argue that the ‘unconscious’ and ‘unacknowledged’ nature of the norms, politics and power that drive global health is a direct byproduct of the processes through which power operates, and a primary mechanism by which power sustains and reinforces itself. To identify what is unconscious and unacknowledged requires more than broadening the disciplinary base of global health research to those social sciences with deep traditions of thought in the domains of power, politics and norms, albeit that doing so is a fundamental first step. I argue that it also requires individual and institutional commitments to adopt reflexive, humble and above all else, equitable practices within global health research.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.042 | 0.036 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".