The Impact of Covid-19 on Nigerian Education System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
At a time when the Nigerian schools are on a temporary closure following the outbreak of the COVID-19 pandemic, this paper looked inwards and reflected on the nature of the education system and revealed its myriads of problems. The COVID-19 pandemic indeed had a huge impact on the educational system in Nigeria. It brought about the cessation of all learning activities in Nigeria except for private universities and secondary schools that swiftly switched to virtual learning platforms. It also illuminated the digital divide between the Nigerian student and his counterparts in other climes. COVID-19 pandemic outbreak also offered an opportunity for the nation to realise the poor status of its educational system. Some of the major problems that have confronted the Nigerian education system, as revealed by this paper, include poor funding, inadequate and dilapidating infrastructure, inadequate teaching facilities, poor teachers' welfare, poor research funding, poor quality of teachers, unconducive learning environment, and the like. The study recommends for the exhibition of sufficient political will by the political leadership for the transformation of the education system as well as the sustained commitment of other stakeholders such policymakers and educational administrators for the transformation of the system to give it its rightful place in our national life.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it