Reality in the Margins, Pseudo-Reality in the Main Frame: The Posthuman in Steven Hall’s "The Raw Shark Texts"
Bibliographic record
Abstract
I contend that, at its core, Stephen Hall’s The Raw Shark Texts is an allegory of reading that illustrates how composite realities exist in the increasingly electronically-dominated world of posthumanism. Hall succinctly identifies how words act upon readers intellectually and psychologically. Readers take the written words from the page and turn them into actual people, places, things, and events within their minds, bringing their own past narratives to create their versions of the text’s pseudoreality. However, the text’s main character, Eric, is disabled by his repeated episodes of complete amnesia – his reality is constantly being erased and rewritten, just like computer memory, leaving Eric with no past narrative to inform his present and future. Hall, very much aware of the conflict between reality and pseudoreality, conflates the worlds of written and digital text, and of human and computer memory in ways that both celebrate their coexistence and warn of one’s potential to eliminate the other. Thus, the allegory of reading exemplifies the potential destruction of reading and the end of electronic posthumanism. As digital text and the mainframe threaten to destroy the act of reading in the twenty-first century, the death of the reader looms large.
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 | 0.002 | 0.005 |
| 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 | 0.041 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".