From Awareness to Conciliation: Education and Policy Coherence between Environmental Issues and Development Imperatives in Africa
Bibliographic record
Abstract
Given the imperative for holistic development (e.g., economic, technology, infrastructure) and the well-being of African populations, this article examines the consequences of incoherent public policies in Africa. These incoherencies are further compounded by environmental and climatic constraints, which are frequently exacerbated by the ratification of international agreements that are incompatible with local priorities. International organizations and frameworks, such as the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the United Nations Framework Convention on Climate Change (UNFCCC), advocate for the importance of sensitization and education as key strategies for addressing environmental and climate challenges. Despite the considerable potential of education, the awareness-raising strategy employed by numerous countries has been relatively ineffective in yielding tangible outcomes. The objective of this article is to demonstrate how an education program that prioritizes conciliation over awareness-raising can more effectively tackle Africa’s development challenges by fostering policy coherence between environmental sustainability and development imperatives. By diverging from the conventional awareness-raising models, this study presents a contextual and comprehensive approach that respects local particularities and is better aligned with addressing environmental and climate crises.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".