Extrapolating Terminology: The Carnivalesque Practice of Language Writing In the Grotesque Body of I Don’t Have Any Paper
Bibliographic record
Abstract
There is a similarity between the rhetorical strategies of Language Writing and the rhetorical strategies attributed to carnivalesque texts by Mikhail Bakhtin. However, the aesthetic differences between standard uses of the carnivalesque and grotesque realism may, at first, obfuscate these similarities in rhetorical strategy. While the aesthetic of these two forms of writing is certainly not identical, there are enough allegorically and rhetorically parallel elements to state that a form of the carnivalesque and grotesque is at work in Language Writing. To prove as much I will summarize Mikhail Bakhtin’s articulation of carnival and grotesque realism and then draw lines of similarity between that articulation and the strategies of Language Writing expressed by Bruce Andrews and Steve McCaffery. In the process I will bolster my argument with reference to textual examples, taken from Bruce Andrews I Don’t Have Any Paper, which exemplify these parallels in operation.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".