Bibliographic record
Abstract
Informed by eighteen months of ethnographic fieldwork in Montmartre, one of the last village-like neighbourhoods in Paris, in this paper, I analyze how people in this community talked through and acted out the COVID-19 pandemic. Using theoretical frameworks from linguistic, cognitive and medical anthropology, I examine “small stories” (Georgakopoulou 2007) about COVID-19, in particular, the analogical and conceptual aspects of this talk. How do people construct understandings of crisis as it evolves? What does this process look like when talk becomes action and reaction and what does it say about the future? This paper explores how people employed analogy, cultural scripts and other linguistic wor(l)d-building tools in their talk about their experiences and comprehensions of COVID-19. Following the arguments of Ochs (2012), I propose that talking about COVID-19 is itself an experience of the virus, an experience that informs people’s understandings of their present circumstances and future possibilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".