The Accelerated Access Initiative to Quality Formal Education for Syrian Refugee Children (AAI). Lessons learned, challenges and the way forward
Bibliographic record
Abstract
Australia, Canada, Germany, Norway, the European Union (EU) the United Kingdom (UK), and the United States (US) have supported the landmark commitment that the Government of Jordan made at the Supporting Syria and the Region Conference, which took place in London on February 4, 2016, to provide quality education for every child in Jordan, regardless of nationality. Over the past three years (2016-19), through the donor-funded initiative ‘Accelerating Access to Quality Formal Education for Syrian Refugee Children’ (AAI), the Government of Jordan is delivering on this promise, providing quality public education to more than 134,000 Syrian refugee children. This effort has included, among others, employing and training new teachers, financing the salaries of teachers and administrative staff, opening additional double shift schools, purchasing school books, covering tuition fees, and covering the costs for operations, maintenance and furniture in these schools. Donors have supported the Ministry of Education (MoE) by combining three different but complementary on-budget and off-budget aid modalities: (1) the EU provides on-budget support; (2) Germany provides off-budget support to the MoE narrowly earmarked for salaries of teachers and administrative staff for double shifts schools for Syrian refugees; (3) Australia, Canada, Norway, the UK and the US provide off-budget support to the MoE through the Accelerating Access Initiative ‘Special Account’ under a Joint Financing Agreement (JFA). Support to the AAI has been extended for one more year. The present report aims to support AAI donors in Jordan to explore potential funding modalities that will enable the Ministry of Education (MoE), donors and education stakeholders to continue to respond to Syrian refugees’ educational needs beyond 2019-20, and to do that in line with the MoE’s own Education Strategic Plan (ESP) 2018-22. The purpose of this report is to: • Assess current AAI aid modalities. • Map out the role and responsibilities of current stakeholders. • Capture lessons learned, challenges and achievement of AAI programming 2016-2019. • Capture preferences and redlines of AAI stakeholders to inform discussions for the next phase of support to Jordan. Three research questions guide this report: 1. What are the roles and responsibilities of current educational stakeholders? 2. What are the achievements and the challenges of the three AAI aid modalities, and what lessons have been learned? 3. What are the donors’ preferences and redlines for future aid support? Research was based on an in-depth literature review on aid modalities that focused on definitions and debates, with a view to proposing a common language and understanding of the key concepts in use. Semi-structured interviews with key educational stakeholders were conducted via phone, Skype or other online platforms between May 28 and June 16, 2019. Interviews were divided in three groups: (i) AAI donors – i.e., Australia, Canada, Germany, Norway, the EU and the UK; (ii) Ministry of Education officials – i.e., the Secretary General, the Development and Cooperation Unit Coordinator, the Research Department Director, the Research and Planning Directorate Director; (iii) other educational stakeholders – i.e., Relief International, Save the Children, UNICEF, the World Bank (WB). Interviews explored several critical issues, such as assessing programme impact against objectives, donor coordination structures, relationship among donors and between them and the Ministry of Education, lessons learned for each aid modality, preferences and redlines for future aid programming.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".