Philosophy in Pieces: The Aphorisms of Nietzsche's Human, All Too Human and Wittgenstein's Philosophical Investigations
Bibliographic record
Abstract
This thesis considers the philosophical importance of the literary form of two aphoristic works of philosophy: Nietzsche’s Human, All Too Human and Wittgenstein’s Philosophical Investigations. Though both these German-speaking philosophers are widely thought to be aphorists, there is little consensus about what exactly is aphoristic about their individual or shared literary forms. While their philosophies and forms of aphorisms are quite different in practice, this thesis argues that Nietzsche’s and Wittgenstein’s modes of aphoristic expression are essential to their philosophical projects in these works. This thesis also explores the particular challenges of interpreting aphorisms in a philosophical context. Though aphorisms have various literary qualities, their status as discrete pieces of philosophy is of greatest interest here. Nietzsche and Wittgenstein match their piecework form of writing to various philosophical goals they set themselves. Their success as highly stylized, aphoristic philosophers is particularly remarkable in light of conventional philosophical writing, which is generally conducted in a much less “fragmented” form. By examining the styles, forms, structures, rhetorics, and interpretations of these two works, this thesis investigates the necessity and practice of their intriguing and difficult modes of expression.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".