Higher Education Governance in Guinea
Bibliographic record
Abstract
The World Bank’s Country Partnership \n Strategy (CPS) for Guinea in FY 2014–171 confirmed the \n Government’s priority to build 21st century skills for \n improved employability and to implement systemic reforms. \n Guinea is emerging from years of political and economic \n isolation and instability. The democratic election of \n President Alpha Condé has opened the door for the \n international donor community, including the World Bank, to \n come forward and support the new government. The World Bank \n will partner with the Government of Guinea to develop \n systems that will ‘improve lagging human development \n indicators for absolute poverty reduction, through more \n efficient and transparent allocation of resources, and to \n build shared prosperity by aligning the business environment \n and education system with Guinea’s economy’ (World Bank, \n 2013, pp. 1). This is in line with the government’s \n priorities, as per the Third National Poverty Reduction \n Strategy Paper (PRSP3) approved in 2013. The PRSP3 aims to \n reduce poverty and to create and sustain a vibrant private \n economy by maximizing rents from Guinea’ssubstantial mining \n sector. The Bank supports the Government’s agenda on \n improving human capital by: (a) promoting both the quantity \n and quality of education, and (b) upgrading skills for the \n needs of emerging and export-oriented sectors such as \n agriculture, tourism, mining, and telecommunications and \n Information and Communications Technology (ICT). In 2012, \n the Government requested special support from the Bank in \n the form of technical assistance to conduct an analysis of \n the higher education system. This analysis will be used to \n prepare a comprehensive higher education strategy to meet \n the needs of both the economy and the labor market. Since \n the early 2000s, the Bank had limited involvement in this \n critical sub-sector. Per the Government’s request, the Bank \n mobilized resources to engage in policy and analytical work \n in the areas of governance, financing, and diagnostic of \n skills demand and supply from a new employer survey prepared \n specifically under this technical assistance project.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".