The contemporary (re-)configuration of global health governance: reevaluating health politics in the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has raised probing questions about the politics that underly health governance. This article engages with recent sociological and political analyses in this regard and offers a reconsideration of what the pandemic represents for global health governance: a moment of reconfiguration rather than continuity or fundamental change. Drawing on Paul Rabinow's notion of 'the contemporary' as a mode of analysis, we conceptualise global health governance as a 'contemporary configuration' and analyse publicly available data from sources documenting the international response to COVID-19. We identify the appearance of a specific kind of problematisation of solidarity in the light of perceived disarray and fragmentation in global health governance. A perception related to the dynamics that evolved in the international political system during the emergency. Further, we discern processes of remediation in global health governance that unfolded around that problematisation of solidarity. We argue that global health governance, as it emerged in the COVID-19 emergency, should not simply be understood as bounded by the past nor as dramatically altered in the present. Instead, it had undergone a reconfiguration wherein solidarity may potentially become the crux of its politics.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.053 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".