COVID-19 Viral Logics, Social Inequality and Hegemonic Mimicry: Deconstructing the Language of Cultural Parasite
Bibliographic record
Abstract
Abstract Drawing on Michel Serres’ philosophical notion of the parasite, this essay examines human responses to COVID -19 that mimic parasitic behavior and uncovers social inequalities by exploring the cultural hegemony of viral logics perpetuated by the media. How can Serres’ notion of the parasite help us reconfigure structural inequalities experienced during the COVID -19 pandemic? First, the essay examines the viral logic of internalization, which seeks to normalize, if not appropriate, the impact of the pandemic through the rhetoric of togetherness. This particular viral logic induces people to internalize the coronavirus pandemic’s illusion as a crisis shared equally by all. The essay argues that this viral logic of internationalization resonates with the French philosopher’s parasite logic, which, in Serres’s words, “expresses a new epistemology, another theory of equilibrium.” Second, this study examines the viral logic of correlation, which designates certain marginalized cultural groups as infected, and therefore regarded and (mis)treated like the virus itself. This blame-game behavior mimics the parasite’s violation of the host’s chain of order and the creation of a new order that is self-serving. Hence, the parasite becomes, according to Serres, “an interruption, a corruption, a rupture of information.” The essay argues that although mimicry becomes the theatre of cultural inequality that dominates communication for the parasitic operator, both viral logics of parasitic mimicry eventually slip into mockery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".