Bibliographic record
Abstract
Nigeria is a nation of people from different ethnic groups and kingdoms, whose mother tongues vary greatly. Indigenous persons with ‘linguistic talents’ played roles as language mediators in interethnic and transnational communication in pre-colonial times. Language mediation was also necessary for successful communication in the colonial period. First, interpreting was prioritised, then translation, a trend which continued into the post-independent era in Nigeria. Today, Nigeria’s significant position, as the most populous African nation with one of the largest economies, puts the country into the global limelight. Effective communication with other countries requires the use of translation and interpreting services. These language services are therefore essential. Recent studies have also established the market demand for translation and interpreting services, providing details of the needs in the industry and the two academic fields. Although further studies are required, current results show developmental gaps in these areas. Accreditation and standardisation issues are pertinent, as are the quality of services currently rendered, and the availability of quality education in academia. In this paper, we trace the history of translation and interpreting in Nigeria, touching on aspects such as religious and literary translation, standards and practices, as well as translator and interpreter studies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.104 | 0.046 |
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".