Bibliographic record
Abstract
Combining the translation theory of Haroldo de Campos and Eduardo Viveiros de Castro’s analysis of Indigenous metaphysics, this essay argues that the metaphorical consumption or cannibalization of texts through translation highlights the role literary influences play in expanding and transforming global literary networks. An understanding of how translated texts consume the source text in the process of their transcreation reveals a rhizomatic exchange and circulation of literature that destabilize at once traditional power structures and conventional translation binaries that give precedence to questions of originality and fidelity. Specifically, attention to rhizomatic literary influences acknowledges the inherent power dynamics and inequalities within postcolonial literature. A cannibalistic view of translation brings into focus these implicit power imbalances while also offering translation as a means to subvert and transform language and cultural hierarchies. Cannibalistic translation recognizes translation as a liminal process of becoming other that transforms not only the source and target texts but also the translator, readers, and literary networks, a process that reverberates through the dialogical relations connecting them all. By drawing on Viveiros de Castro’s works on Indigenous Amazonian ontologies, this article demonstrates ways in which the cannibalistic translation theory of the de Campos brothers can continue to be refined.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".