Challenges and Solutions of Higher Education in the Eastern Caribbean States
Bibliographic record
Abstract
Higher education is considered as one of the most essential factors in influencing societal changes, due to its ability to help formulate good decision making in every sphere of modern society, in businesses, education, politics and science. Higher education over the years has significantly increased, thus given rise to many opportunities for those who pursue it. The Caribbean students, like the rest of the world seek to benefit from higher education, not only for enhanced academic knowledge, but also for socio-economic development.Due to its sluggish development, brittle economy and lack of natural resources the Caribbean region faces many economic challenges in making quality higher education accessible to all of its occupants.The purpose of this study is to investigate and compare the challenges of low output of higher education and availability of higher education institutions in the 21st century in the Eastern Caribbean. The study analyzed database of 37 tertiary institutions in the OECS, while using comparative approach to analyze availability and cost for higher education. Results show that factors that are affecting higher education in the region are accessibility, location, quality of education, institutional costs and unemployment of graduates. We found that increased access in higher education has risen tremendously due to accessibility of technology and factors like globalization, integration-networking and traveling cost. This paper suggests that collaborative approach be taken by governments of the region to increase access and funding for higher education through scholarships and grants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".