The Transductive Flu: Disruptive Virality and Biosocial Immunity
Bibliographic record
Abstract
This thesis explores the multifaceted nature of the flu virus, examining its biological, socio-cultural, and cyber-technological dimensions to understand how these viralities might interact and shape both the virus's potential becomings, and our responses to it. Methodologically, the study employs an ethnographic approach, relying on intentional explorations and spontaneous encounters in the context of a multi-site fieldwork that mostly took place in Ottawa—including visits to a doctor's office and a pharmacy, conversations with various healthcare professionals and volunteers, as well as digital encounters. Findings highlight that effective collective immunity against the flu requires a multimodal understanding and approach that integrates biological, social, and cyber-technological perspectives, and that building adaptive, flexible resilience involves fostering community connections through shared experiences and relational gestures, rather than relying solely on methods of isolation and control.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".