Finding Archaeological Relevance during a Pandemic and What Comes After
Bibliographic record
Abstract
This article emerged as the human species collectively have been experiencing the worst global pandemic in a century. With a long view of the ecological, economic, social, and political factors that promote the emergence and spread of infectious disease, archaeologists are well positioned to examine the antecedents of the present crisis. In this article, we bring together a variety of perspectives on the issues surrounding the emergence, spread, and effects of disease in both the Americas and Afro-Eurasian contexts. Recognizing that human populations most severely impacted by COVID-19 are typically descendants of marginalized groups, we investigate pre- and postcontact disease vectors among Indigenous and Black communities in North America, outlining the systemic impacts of diseases and the conditions that exacerbate their spread. We look at how material culture both reflects and changes as a result of social transformations brought about by disease, the insights that paleopathology provides about the ancient human condition, and the impacts of ancient globalization on the spread of disease worldwide. By understanding the differential effects of past epidemics on diverse communities and contributing to more equitable sociopolitical agendas, archaeology can play a key role in helping to pursue a more just future.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".