Basic Education in Ivory Coast: From Education for All to Compulsory Education, Challenges and Perspectives
Bibliographic record
Abstract
Ivorian authorities, for years, are employing various strategies as part of reforms to ensure universal education in Ivory Coast (Cote d’Ivoire). In this regard, great efforts are done each year through public funding and partnership development support to face the challenge of Education for All whose term of the implementation was 2015. The objective of this paper is to investigate the various facets of Education for All in Ivory Coast and the implications of the implementation of compulsory education, which is the new challenge of Ivorian education system. How to bridge the gap of schooling? What measures would be effective to ensure 100% enrolment as multiple factors constitution obstacle to the achievement of the Millennium Development Goals (MDGs). Using qualitative methods based on relevant data from books, articles and others secondary sources from reports as well as other information from the World Wide Web, this study examined the current issues of Education for All in the post-crisis context. Although immense sacrifices have been done to Education for All, persistent factors unlikely still limit its implementation. The success of the compulsory education is possible whether the State invests more resources in Education and creates well conditions for access to education in all areas of the country, paying more attention to marginalized groups such as children with disadvantaged social backgrounds and girls. In conclusion part of this study possible solutions and recommendations that can overcome the persistent issues of Education for All are provided for higher educational policy prospects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".