Bibliographic record
Abstract
Translation is a significant factor in cross-language research and can make an overarching impact on research outcomes and ethical adequacy. In this article, the author examines ethical considerations of language translation and interpretation in human science research. The focus is narrowed to ethical aspects of research design in which a bilingual researcher assumes a double role, as an interpreter and translator in his or her inquiry with monolingual, non-English-speaking research participants. The bilingual researcher has a great advantage of expertise and clear vision in the cross-cultural ethical environment. However, such clarity of the ethical vision can cause many reasonable doubts, because it might not fully fit into the framework of standardized, rational ethics. The author shows that the dualism of the bilingual researcher's position can either induce caution or carry solutions, but both aspects of this role contribute to its essential significance for good cross-cultural knowledge exchange.
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 | 0.096 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| 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".