TRANSLATOR-TRAINING IN SOME NIGERIAN UNIVERSITIES: CHALLENGES AND RECOMMENDATIONS
Bibliographic record
Abstract
This paper focuses on the Nigerian Translator-training programmes and its challenges. It is a descriptive research premised on a problem raised by Akakuru that most translation graduates from Nigerian universities can neither translate nor talk about translation. This paper sets out to investigate the validity of the statement and to point out where the problems lie as well as propose some solutions to them. Data for this work were collected from websites of some Nigerian universities that train translators up to the Masters and Ph.D. levels. Data were also collected from the websites of some renowned African, European and Canadian universities that equally train translators up to the Masters and Ph.D. levels and whose graduates are performing well in the field of translation in and out of their countries of origin. A comparative analysis of the data collected reveals some lacuna in the Nigerian Postgraduate Translator-Training Programmes at the levels of the trainees, the trainers and the content of the programmes themselves. The paper further suggests that the way forward for a successful translator-training programme in Nigeria is to adapt and implement the European and the Canadian models.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".