ARABIC LANGUAGE IN GOVERNMENT AND PRIVATE SCHOOLS IN NIGERIA/ اللغة العربية في المدارس الحكومية والأهلية في نيجيريا
Bibliographic record
Abstract
Private and Public schools have contributed immensely to the growth and development of Arabic Language in Nigeria. Even though the method of teaching in the private Arabic school before the advent of the colonialism in Nigeria was informal, Arabic developed to the extent of being used to produce literary works. In the first quarter of the twentieth century, formal private Arabic schools, as well as the public ones, were established in the country. From then, especially after the independent of Nigeria, the situation of Arabic schools is changing for the better. That notwithstanding, these schools are still encountering problems. The aim of this study, therefore, was to examine the current situations of Arabic language and its problem and prospects in Nigerian private and public schools vis a vis its growth and development. The study concluded that while the case of Arabic in Private Schools in Nigeria is perfect, it appears dismal in public schools. It is therefore recommended that the governments’ recognition and support for Arabic schools and programmes and adequate review of Arabic curricula Arabic will attain the greater height in Nigeria.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".