Les Tècniques de traducció (dels referents culturals) : retorn per a quedar-nos-hi
Bibliographic record
Abstract
Aquest article té com a objectiu fer una revisió d'algunes de les classificacions de tècniques de traducció que s'han proposat en la literatura, agrupar aquestes classificacions en tipus (d'acord amb el criteri subjacent), discutir la conveniència de fer servir classificacions generals (vàlides per a qualsevol problema de traducció) o específiques per a cada problema i, finalment, fer una proposta de tècniques de traducció dels referents culturals (com a problema concret que pot servir d'il•lustració dels punts anteriors).En tot treball descriptiu, el concepte de tècnica de traducció ha d'ocupar una posició central (juntament amb la tipologia del fenomen estudiat i els factors que incideixen en la presa de decisions) a l'hora de determinar les regularitats o normes en la conducta traductora.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".