Bibliographic record
Abstract
With the advent of technology, the triumph of immediacy, and the emergence of an environment of epistemic humility, a consensus has grown in some literary and academic quarters that literature is either dead, at death’s door, or at best in intensive care. This paper argues that this kind of diagnosis and autopsy of the discipline is not due to the irrelevance of the old forms of literature in today’s world, but rather to a failure of nerve and imagination in the face of immediacy and market temptation. The different scenarios and attempts to digitize the printed book through ebooks and “biterature” result in the literature of the future rather than the future of literature; the value of literature lies in its ability to challenge, rather than reinforce, our world assumptions. My argument is that for literature to be healthy and continue to thrive in today’s environment, it should not be chameleon-like and forced to adjust its values to the tailored needs of the information and immediacy age or retreat before the forces of consumer vacillation. Instead, it should cling to its fundamental value of taking the lesser travelled path, no matter how maladapted, and writers and scholars should take remedial action against literature’s unhealthy environment and habitat.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".