INNOVATION IN FINANCING GEOGRAPHY EDUCATION FOR SUSTAINABLE DEVELOPMENT IN 21st CENTURYNIGERIA
Bibliographic record
Abstract
It is a common knowledge that governments of various nations particularly developing countries including Nigeria find it difficult to dedicate 26% of their annual budgets to education as specified by United Nations Education Social and Cultural Organization (UNESCO) standard, hence it is obvious that such governments can no longer adequately fund education to meet the desired sustainable development of the 21st century. Therefore, there is the increasing need for collaboration between public and private entities to ensure education is well funded to meet the expectations of citizens. It is against this background that this paper focuses on “Innovation in Financing Geography Education in Nigeria for Sustainable Development in the 21st Century”. In order to achieve this goal, the paper highlighted the concepts of innovation in financing education, geography and education. Similarly, the paper looked at the role of geography education in the society and the concept of sustainable development. Also discussed were innovation in financing geography education for sustainable development and challenges that limit the level of innovation in financing geography education in Nigeria. The paper concluded that to achieve sustainable development through innovative financing in geography education more mobilization of resources through existing and new financial sources must be adopted going forward. Amongst others it was recommended that the government should create an enabling environment for a more holistic collaboration between the public and private sectors in order to enhance and improve geography education to meet the challenges of the 21st century.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".