Pandemicity and Subjectivity: the posthumanist vulnerability of the zoe/geo/techno framed subject
Bibliographic record
Abstract
The COVID-19 pandemic has caused unprecedented global disruptions, including a fundamental alteration to how humans exist. In this paper, we argue that the disruptions brought forth by the pandemic have provided us with a new perspective that allows us to better understand the various entanglements that are constitutive of the beings we are, but that also render us fundamentally vulnerable. Grounded in a posthumanist material feminist position, we adopt a view of matter as entangled and embrace the notion of agentic capacity while elaborating a definition of posthumanist subjectivity and its peculiar vulnerability. Building our analysis on Rosi Braidotti’s formulation of the zoe/geo/techno assemblage, we further develop this frame navigating through the different entanglements that constitute the posthumanist subjectivity we scrutinize, considering each type from the perspective of the pandemic. As we argue, the increase in one type of entanglement at the expense of others may be generative of new possibilities but can also limit our thriving. What defines us as humans is the fact that we are constituted via the threefold entanglement of zoe, geo, and techno, radically boosting one and diminishing the others—purposefully or not—is bound to have significant impacts. Further, we claim that we cannot in fact isolate one type of entanglement from the others: each impacts the other as they themselves are also entangled.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.094 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".