Bibliographic record
Abstract
This essay is the first to historicise and give a comprehensive assessment of literary responses to cognitive overstimulation. A wave of post-war writing responded playfully to informatics in a pre-digital period through an engagement with physics, entropy and post-structuralist theory. In an era dominated by neuroscientific revolutions, fiction written in the digital age addresses the pressing information overload debate with a new seriousness, often stressing concerns about the impact on the human mind. Contemporary fictional writing depicts an increasingly immersive online experience that accelerates information processing by human minds under technostress. New phenomena such Big Data and ‘infobesity’ affect not only writing practice but stretch the mainstream novel form to its representational limits. Mainstream literature is critical of the changing the shape of our lives and minds at the level of content, yet fails to find new forms of storytelling. This essay ends by identifying new writing that unites form and content in innovative ways through storytelling modes that represent the processes we are living through more accurately. We are experiencing a major epistemological shift, and are witnessing the emergence of exciting, new kinds of subjectivity.
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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".