Breve análisis sobre la autotraducción en América Latina
Bibliographic record
Abstract
Los autotraductores confieren a sus obras la autoridad que supone la autoría, algo de lo que están faltas las traducciones normales. De hechosus respectivas obras son únicas, aunque las lenguas que empleen sean dos o más. Debido a las dificultades en encontrar autores emigrantes que se autotraducen en América Latina, el ensayo analiza las autotraducciones de algunos autores latinoamericanos, emigrante so hijos de emigrantes de Canadá y Estados Unidos así como las de los autores indígenas principalmente mexicanos.A Brief Analysis of Self-Translation in Latin-AmericaSelf-translators provide their works with the same authority implying paternity, which is generally missing from usual translation practice The respective works are, in fact, unique, although the languages used are two. Because of the difficulties in finding in Latin America migrant authors who self-translate their works, this essay analyses the self-translations of some Latin American authors – immigrant or immigrants’ children – living in Canada or the USA together with autochthonous, mainly Mexican, authors.Breve analisi sull’ autotraduzione in America LatinaGli autotraduttori danno alle loro opere l’autorità che implica la paternità, qualcosa che manca alle traduzioni normali. In effetti, le loro rispettive opere sono uniche, sebbene le lingue che usano siano due o più. A causa delle difficoltà nel reperire autori migranti che si autotraducono in America Latina, il saggio analizza le autotraduzioni di alcuni autori latinoamericani, immigrati o figli di immigrati in Canada e negli Stati Uniti, nonché quelle di autori autoctoni, principalmente messicani.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".