Water, Food, Shelter and a Mobile Phone <i>Mobile Learning Despite Crises Syrian Refugees' Case Study</i>
Bibliographic record
Abstract
This panel describes the refugees’ crisis and its impact on school age children. The focus is on the Syrian children refugees in Mount Lebanon, an area that is usually forgotten. The United Nations offers schooling to primary school children in this remote region, but lack of resources in Mount Lebanon schools is evident, access to technologies and applications integration is very limited, and teachers’ frustration is obvious. There are a quarter of a million Syrian refugees in the country who still do not have access to formal education in the Lebanese school system. The country is looking to integrate and develop better educational opportunities to provide better access to education via technologies. Quality education is the key to achieve sustainable development in all aspects, especially if this continues in emergency and crises, this was the topic of discussion at the UNESCO headquarter in Paris during the Mobile Learning Week 2017, and the presented case study is to deal with the refugee crises and how to better provide teaching and learning opportunities via mobile.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".