<i>The Performance of Reading: An Essay in the Philosophy of Literature</i>by kivy, peter
Bibliographic record
Abstract
Google Scholar Peter Kivy's bold thesis in this very engaging and stimulating monograph is that literature in general—not just drama or even poetry but also the novel—should be viewed as a performance art, analogous to music, where the instances of works through which they are appreciated are performances enacted by readers. Reading itself, then, is a performing art. Kivy argues for a number of analogies between fictional narratives and musical scores. The literary text, he maintains, is not a token of the literary work but, like the musical score, a character in a notation for generating instances of the work. Tokens of the musical work are performances, and tokens of the novel are readings, construed as datable events. Furthermore, Kivy argues, silent readings of novels are analogous to performances of musical works “in the head” by score readers. Performances of literary works “in the head” can be seen as expressive “soundings” that, as in the case of “soundings” of musical works by score readers, embody an interpretation of the overall sense of the work. Given that the silent reading of a novel can be viewed as a performance, Kivy further maintains, this is how it should be viewed: literature in general, including the novel, is properly viewed as a performance art.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.014 |
| 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".