Bibliographic record
Abstract
Since 2010, there has been a discernable expansion of global health financing forms using private equity, bonds, and ‘facilities’ to finance international development and humanitarian endeavors. I present the logics of the Pandemic Emergency Facility (PEF), a World Bank device that lashes together a bond, cash, and swaps to lie in reserve for an infectious disease outbreak. I explain how the PEF is emblematic of financial devices that have the potential to fund global health aid while offering investors a chance to make money. Reckoning with the pandemic bond means that we take account not only of the PEF (what does it organize and by what logics?) but also of the relationships it cultivates (what does it bind together?) and reproduces (what does it aim to multiply and what does it forsake?). I use ‘reckoning with’ as an analytic concept to help us think about measures and futures of global health in both economic and ethical registers, as well as to take account of how death data is used. Reckoning with something lets us pause to take account of where we are and where we are going, and helps us think about what we want. Is it necessary to translate the ethical obligation to help those who are suffering into financial devices that make people money, a trend we are clearly in the initial stages of? Are there conditions when the suffering of others as the source of financial speculation becomes desirable?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 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".