The (non-)translation of English-language, embedded social media quotes in Norwegian and Spanish online news texts: Effects of assumed levels of reader proficiency?
Bibliographic record
Abstract
In this article, we investigate journalist-translators’ strategies in dealing with English-language embedded social media quotes (ESMQs) in Norwegian and Spanish online news texts. Embedded social media quoting is a relatively new, but fast-growing phenomenon, which has yet to receive much scholarly attention. A few possible reasons for the (non-)translation of ESMQs in their surrounding main texts have been suggested in the hitherto scant literature, but one important hypothesis has not yet been introduced, namely that journalist-translators’ assumptions regarding the degree of English proficiency in their readers might influence their decisions regarding whether to translate (and how to translate). In this article, we present a comparison of the (non-)translation of 120 ESMQs in Norwegian vs. Spanish online news texts, which shows that there are in fact no significant differences in the rate of non-translation of these quotes, despite the fact that Norway and Spain are countries where English proficiency is assumed to be high vs. low, respectively. This means that assumed proficiency in the source language does not play the expected role in guiding journalist-translators’ decisions in our study, except if one can say that non-translation is chosen for two different reasons in the two groups: 1) a high degree of faith in the English proficiency of readers (in the Norwegian case), and 2) a low degree of confidence in one’s own English proficiency leading to translation avoidance (in the Spanish case). We conclude that qualitative research looking into various groups of journalist-translators’ motivations for (non)translation is needed.
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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 | 0.017 | 0.199 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".