The Missing Post-Humanism: A Philosophical Posthuman Study of Don DeLillo’s Zero K and William Gibson’s Pattern Recognition
Bibliographic record
Abstract
In the Anthropocene, humans have established an uninhabitable ecosystem. His insatiable desire for resources and possessions has led to this catastrophe. The posthuman studies critically examine the human-nonhuman divide through technology, biology, and culture. The inquiry raises ethical and ontological questions about human enhancement, artificial intelligence, and the social effects of new technologies. Posthumanism envisions a complex and interdependent world. Francesca Ferrando believes posthumanism threatens the anthropocentric worldview. Posthumanism reconsiders human identity, agency, and existence in light of emerging technologies and complex human-nonhuman relationships. It emphasises inclusivity, connectivity, and subtlety in human experience. She demonstrates post-humanism, post-anthropocentrism, and post-dualism in her seminal idea of ‘Philosophical Posthumanism’. This study analyses Don DeLillo and William Gibson's science fiction novels to reconsider “the human.” In this paper, Don DeLillo's Zero K and William Gibson's Pattern Recognition, Ferrando's ‘philosophical posthumanism’ and ‘posthumanism’ based on post-dualism, post-anthropocentrism and post-humanism views are compared. Ferrando's seminal work on 'Philosophical Posthumanism' expands on the above argument. This study seeks to investigate the absence of Ferrando's concept of post-humanism in selected literary works and the need for interdependence with other non-human species.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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 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".