Pandemic Life-lines: A Multimodal Autoethnography of COVID-19 Illness, Isolation, and Shared Immunities
Bibliographic record
Abstract
As a crosscutting concept in biology, anthropology, and philosophy, immunity has been a critical ‘site’ of debate on the relations between self and other, organism and environment, risk and responsibility, the corporeal and the political. In this Research Article, I trace how these relations and everyday life during the COVID-19 pandemic relied on a web of coordinated—and sometimes unexpected—lines of communication, restriction, and solidarity. Using an experimental approach that combines multimodal autoethnography and multiscalar relational analysis, I present a first-person account of travelling during, testing for, and falling ill and isolating with COVID-19 in late 2021. I explore how pandemic life-lines, including public health measures, vaccinations, devices, and helplines, as well as mundane gestures of care and ecologies of support, acted together as shared immunities. In this exploration, I propose to reconceptualise ‘immunity’ as a process network rather than a defence apparatus, shedding light on how these life-lines may influence differential trajectories of disease and healing. To conclude, I discuss how my conceptual and methodological approach contributes to a social ecological understanding of immunity, that goes beyond the biopolitical, in times of pandemic and in the 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".