Richard Wright's search for a counter-hegemonic genre: the anamorphic and matrixial potential of haiku
Bibliographic record
Abstract
Examining Richard Wright's vast haiku oeuvre, this article shows how he used haiku to reinvent the form in English by repeating playfully anamorphic imagery so as to construct a poetic matrix modelled on, but distinct from, the categories he saw used in Japanese haiku. Writing haiku in this productive manner produced Nietzschean jouissance rather than resentment, so that the affective dimension of a multitude of socio-economic relations, including race, could be demonstrably reframed in this matrix of anamorphic imagery which maintained political allegories and critical consciousness in the landscapes of his invention. He asserted measured displacements and anamorphic transvaluations of how one sees (in Jacques Rancière's sense), and as such presented a modernist haiku constructed not in isolate verses (as haiku are often read), but in refrains and nodes, suggesting an intertextual matrix asserting new commonplaces (locus communis) and self-evident ways of seeing. This article also points out the lack of evidence for the current consensus in literary criticism which mistakenly asserts that Wright discovered a Zen spirit which elevated his spirit and brought him closer to nature. Moreover, the editing of the book manuscript is shown to be not only flawed in its critical framing, but in the very ordering of haiku, presentation, and even title.
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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 | 0.006 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".