Hybrid languages, translation and post-colonial challenges
Bibliographic record
Abstract
Multilinguality, creolization and hybridisation are central phenomena of language. Languages bear the traces of creative borrowing, as well as forced changes as a consequence of domination. However, mainstream translation theory generally seems to presuppose a clear division between source language and target language. Why the persistence not to see the ways languages intermesh? In this essay I 1) argue the importance of multilinguality for translation theory, 2) suggest that the theoretical insistence in not seeing the way languages intermesh is grounded in an Occidentalist preoccupation with I/Other relations which require strict dichotomization and 3) disrupt the dichotomy with insurgent texts and voices which countermand the tendency to erase any kind of linguistic mixing. I conclude by proposing a methodology for taking fuller stock of the play of power and the plurality. Received: 08-04-07 / Accepted: 06-08-07 How to reference this article: Price, J. M. (2007). Lenguas híbridas, traducción y desafíos poscoloniales. Íkala. 12(1), pp. 61 – 93.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".