Translating across Lines of Identity and Domination: The Case of M. NourbeSe Philip's <i>Zong!</i>
Bibliographic record
Abstract
This article examines the case of the recent ‘unauthorised’ translation of Caribbean-Canadian poet M. NourbeSe Philip's Zong! (2008) into Italian, which was at the centre of a heated debate on authorship and coloniality. The author unravels the rhetoric used in clashing arguments and identifies underlying values and assumptions to expose the system of power and the historical context in which this confrontation took place. Through an investigation of questions of authorship, authority and ethical posture, the author draws a distinction between a legalistic and a relational paradigm of translation. The article foregrounds the critical role of identity in the translation process and uncovers the enduring of colonial, racist and sexist structures embedded in the international publishing world, exposing both the limits and the creative potential of translation as a cultural practice that is deeply political.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".