Education dimensions relevant to successful electronic levy mobilization in resource-rich yet poor countries in Africa
Bibliographic record
Abstract
First and foremost, the study explored why countries in Africa are rich in natural resources yet resort to e-levy legislation for more revenues. In addition, the study investigated dimensions of education needed to facilitate successful mobilization of e-levy revenue in resource -rich yet poor countries in Africa. Qualitative exploratory design, semi-structured interviews, judgmental and snowball sampling techniques were used for the study. Twelve (12) scholars from US (N = 3), Uganda (N = 3) Canada (N = 3), Ghana (N = 3) were interrogated. The paper was guided by the natural resource-cursed and social learning theories. Thematic analyses were used to analyse the data. It was found that although African countries are rich in natural resources yet they face challenges generating revenue from natural resources due to mismanagement, poor leadership and weak governance. They also find it difficult to mobilize revenues from e-levy too because of the informal nature of the economy, lack of financial inclusion, corruption, the disinterest of the public in the e-levy legislation as well as inadequate education on the e-levy concept. But the advanced economies are successful in generating revenue from e-levy. Proactive leadership and governance in managing natural resources, addressing mismanagement, and dealing with corruption and its negative effects are required to make things happen in Africa. African economies need to be more formalised and financial inclusion deepened. Proper accounting of state revenues to the citizenry must be enforced. E-levy education, civic education, digital literacy, ethics and legal education, can significantly contribute to the success of e-levy revenue generation in Africa.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".