Translation studies in German as a foreign language (GFL) in Africa (Nigera): A tool against "waiting room dangers"
Bibliographic record
Abstract
The importance of translation in Additional Language Learning (ALL) cannot be overemphasized, as there exist of late various studies in support of this fact. Extant studies on this issue have dealt with various aspects of translation in language learning in Europe, Canada, Australia and in the United States, although studies in this regard are not limited to these geographical boundaries. Moreover, these studies do not only focus on the impact of translation on ALL but also on various other issues like plurilingualism as it relates to translation in ALL. However, research in this regard are relatively scarce in the African context. As such, many studies done in Africa, particularly in Nigeria, are limited in scope when it comes to the relevance of translation in learning German as a Foreign Language (GFL). This study therefore seeks to shed more light on how translation studies in GFL lessons in Nigeria could be useful as a tool against “waiting room dangers”. In this sense, I mean the (in)security issues encountered in places of temporary localisations.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".