The Pandemic as a Gateway to the Posthuman in the Digital Novel <i>The Silent History</i>
Bibliographic record
Abstract
The COVID-19 pandemic has been explored from different standpoints and, like many preceding pandemics, often interpreted as a rupture, undermining faith in human progress and exploring human vulnerability. Literature has also traditionally reflected pandemics in this light. However, with the rise of post-human studies, new ways of thinking about pandemics have emerged, inviting a re-evaluation of what it means to be human. As a situation of, and metaphor for, transformation, pandemics challenge traditional humanist narratives, offering new forms of identity, agency, and consciousness and providing new ways to reflect the human/non-human entanglement and the relationship between humanity, technology, and the environment. This article focuses on The Silent History, originally published as a touchscreen serialized novel that depicts a pandemic that renders children unable to use and understand language and challenges restricted definitions of what counts as human. The novel explores a post-anthropocentric world-view, questioning the centrality of language to human identity and experience as well as the conception of life as a continuous line of enhancement. The Silent History challenges entrenched notions of humanity and conventional forms of storytelling as it combines the technological affordances of iPhones and iPads through images, interactive maps, sounds, videos, presentations, and GPS technology, with a string of serialized character “testimonials.” The pandemic is not presented as a rupture but, rather, as a gateway into a new human condition akin to a networked existence, where human identity is redefined through its entanglement with technology, the environment, and other non-human entities.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".