Higher Education and Its Contribution to Economies of African Countries: Move Towards Competence-Based and Skills Demand-Driven Standards in Collaboration with Industry
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
This study explores the ecosystemic impacts of higher education (HE) on the economies of African countries, emphasizing the need for competence-based, and skills-demand-driven standards in collaboration with industry. HE is vital for equipping individuals with essential knowledge and skills for socio-economic transformation. However, in Africa, this role has weakened, with industry assuming a leading position. Curricula in HE institutions are slow to adapt to the skills needed by industries, leading to a range of challenges such as outdated curriculum delivery, desertion of technical and vocational training, inadequate research resources, insufficient collaboration frameworks between HE and industries, minimal support for entrepreneurship, and poor infrastructure. Aligning HE curricula with industry skills requirements is crucial for enhancing African economic development and competitiveness. Unfortunately, there is a notable lack of partnerships and practical mechanisms for curriculum integration among African HE institutions, which results in graduates possessing skills that do not meet industry demands. This paper reviews the extensive literature on HE's role in African economies, advocating for in-depth collaboration between HE and industry in order to tackle skills mismatches. Accordingly, establishing a healthy partnership between HE institutions and industries could facilitate work-integrated learning, encourage industry-led curriculum development, and prepare graduates with applicable skills and relevant knowledge for the job market. Thus, developing a proactive framework that can facilitate and enforce collaboration between higher education and industries could be critical in addressing the challenges faced by African economic development.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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