Bibliographic record
Abstract
In 1962, the philosopher Richard Taylor used six commonly accepted presuppositions to imply that human beings have no control over the future. David Foster Wallace not only took issue with Taylor's method, which, according to him, scrambled the relations of logic, language, and the physical world, but also noted a semantic trick at the heart of Taylor's argument. Fate, Time, and Language presents Wallace's brilliant critique of Taylor's work. Written long before the publication of his fiction and essays, Wallace's thesis reveals his great skepticism of abstract thinking made to function as a negation of something more genuine and real. He was especially suspicious of certain paradigms of thought-the cerebral aestheticism of modernism, the clever gimmickry of postmodernism-that abandoned "the very old traditional human verities that have to do with spirituality and emotion and community." As Wallace rises to meet the challenge to free will presented by Taylor, we witness the developing perspective of this major novelist, along with his struggle to establish solid logical ground for his convictions. This volume, edited by Steven M. Cahn and Maureen Eckert, reproduces Taylor's original article and other works on fatalism cited by Wallace. James Ryerson's introduction connects Wallace's early philosophical work to the themes and explorations of his later fiction, and Jay Garfield supplies a critical biographical epilogue.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".