Perspectivizing pandemics: (how) do epidemic histories criss-cross contexts?
Bibliographic record
Abstract
Abstract This article explores a smattering of thematic questions that criss-cross the articles in this special pandemics issue; it signposts some reverberations, overlapping responses, and problematic comparisons currently (mid 2020) being made between past pandemics and the tense experiences (and projections going forward) of COVID-19 across the world. The historical pandemics covered here offer an entry point to a fruitful set of genealogies, chronologies, epidemiologies, trajectories, and imaginaries linked to a host of issues: what makes a pandemic ‘global’? What does a global history perspective bring to the table? How does examining germs and genomes shed light on imperialism as a/the pandemic driver? Where do animals, the environment, and ecology fit in and why are they so often excluded from pandemic histories? What counts as medical humanitarianism when health knowledge, know-how, and cooperation ‘from below’ are sidelined? And what came/comes first: a pandemic or a changed world?
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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".