Island‑to‑Island Translations in the Contemporary Literary Capitalocene: Navigating the Politics of In/Visibility and Discoverability
Bibliographic record
Abstract
Drawing on professional experiences as a literary translator seeking to promote transversal, island‑to‑island translation flows, together with a comparative analysis of the contemporary literary ecosystems of the insular Caribbean and the Indian Ocean, this article pits recent debates on the issue of in/visibility in Translation Studies against concrete examples of translations marketed for English‑ and French‑speaking readers. In particular, it focuses on the recent co‑translation, for the Irish/UK market, of Reunionese writer Gaëlle Bélem’s debut novel, Un monstre est là, derrière la porte , and on the translation, for the French/Antillean market, of Trinidadian writer Elizabeth Nunez’s memoir, Not For Everyday Use . It highlights patterns present behind the concept of “book discoverability” to examine issues of asymmetry and invisibilization at work in the contemporary “literary Capitalocene,” and concludes with a call for a new book order in which translation can be (re)envisaged beyond the (neo)colonial doctrines of visibility and discovery.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.069 |
| Scholarly communication | 0.032 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".