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Record W3135802005 · doi:10.38140/jtsa.2.4275

Translation studies in German as a foreign language (GFL) in Africa (Nigera): A tool against "waiting room dangers"

2021· article· en· W3135802005 on OpenAlexaboutno aff
Omotayo Fakayode

Bibliographic record

VenueJournal for Translation Studies in Africa · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsGermanContext (archaeology)Scope (computer science)Relevance (law)Political scienceTranslation studiesMultilingualismLinguisticsComputer scienceHistoryPhilosophyLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.190
GPT teacher head0.378
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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