Damned if you do, damned if you don’t: The politics of pandemic preparation as a grand challenge
Bibliographic record
Abstract
In this article, we shed light on the politics of preparation for a pandemic. We show why the existing literature fails to explain the puzzling case of France, which became one the best prepared countries in the world, only to discontinue its efforts to find itself unprepared when COVID hit. To investigate what happened, we conduct a qualitative process-tracing analysis of pandemic preparation efforts for two decades. From this, we induce the causal mechanisms at work during this period and we develop new insights on adverse events preparation and mitigation. Our main contribution is to conceptualize pandemic preparation as an insurance which would reduce future costs only in certain conditions. Given this particularity, we contend that governments take significant electoral risks when they engage in such an endeavour. If preparation is successful, it is likely to remain largely invisible and bring no electoral credit. If in contrast, the feared event does not happen or if its effects have been overestimated, a mechanism of blame generation for having wasted public money will be at play. Finally, if governments discontinue preparation and the dreaded event occurs, they will be blamed (again) for this discontinuation. Hence, governments risk being blamed twice when engaging in pandemic preparation, which explains why governments rarely prepare enough.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".