Analysis of Employment Realities in Nigeria in the Midst of Covid-19 Pandemic
Bibliographic record
Abstract
Throughout global efforts to tackle COVID-19, it’s been clear that job losses put more people at risk, socially and economically. Welfare and job security to fully combat this health crisis are tools that need to reach everyone, everywhere, equally but this is farfetched in the Nigeria society. Today, there are hundreds, if not thousands of pathetic stories relating to retrenchment and loss of jobs. Before the emergence of COVID-19, Nigeria’s unemployment rate was already really high at about 23.1% meanwhile underemployment was considered at 16%, according to a 2018 report by the National Bureau of Statistics (NBS). In the third quarter of 2019, Minister of Labour and Employment, Chris Ngige indicated that the federal government forecast an unemployment rate of 33.5% by 2020. Sequel to the above, it is pathetic to indicate that the outbreak of COVID-19 pandemic has resulted to the outnumbering of the anticipated percentage of unemployment and unpalatable employment realities in Nigeria made manifest in human resource development gap, working from home, salary reduction and deferment of bonuses, denial of promotion, unpaid leave, declaration of redundancy, termination.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".