Bibliographic record
Abstract
In “Performance Philosophy — Staging a New Field,” Laura Cull approaches performance as a source of philosophical insight and philosophy as a species of performance (Cull 2014, 15). This calls for a radical transformation of philosophy and its practices. What form might this take? Wittgenstein’s later philosophy provides one example. The language games presented in the opening remarks of the Philosophical Investigations (PI, [1953] 2001) are meant to be played out. They involve improvisation based on general scenes, stock characters, and linguistic play. When enacted, they are slapstick. As such, they offer a method of philosophical investigation in which clarity and insight are inherent in the performance itself. Wittgenstein’s language games were directly influenced by the subversive practices of Austrian commedia dell’arte and slapstick (through the works of Johann Nestroy and Karl Kraus). By their very nature, they challenge the pretensions of philosophical explanation and theory. Unlike attempts to compare Wittgenstein’s philosophy to theatre, enacting language games is a form of philosophical performance. Andrew Lugg notes that recent attempts to compare Wittgenstein’s philosophy to theatre problematize the opening remarks of the Investigations. However, enacting language games as a form of philosophical performance makes what is hidden, in all of its simplicity and familiarity, obvious, striking, and engaging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".