Dar la palabra a los intérpretes: el mito de la neutralidad y otros factores contextuales que pueden afectar su desempeño
Bibliographic record
Abstract
Como lo muestra Hsieh (Hsieh 2016), los factores contextuales que pueden influenciar el desempeno de un interprete en el campo de la salud han sido poco estudiados. Este es precisamente el objetivo de nuestro articulo. Metodo: Efectuamos un analisis tematico de los relatos de veinticuatro interpretes de Montreal (Canada), divididos en cuatro grupos de discusion. Resultados: Sus discursos gravitan alrededor de once factores contextuales que pueden afectar su desempeno, repartidos en tres temas: consulta interpretada, principios eticos y condiciones de trabajo. Discusion: La mayoria de los once factores tiene que ver con la calidad de la relacion interprete-profesional, afectada, entre otras cosas, por la falta de tiempo multiforme alrededor de la consulta y por la presuncion de que los profesionales buscan en el interprete un conducto. Este campo necesita un cambio conceptual, en particular a proposito de la neutralidad del interprete, que no es un mito perjudicial como lo propone Angelelli (Angelelli 2004), sino un potente vector relacional.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".