Providing access to education in Sub-Saharan countries through Content-Oriented technology
Bibliographic record
Abstract
Access to education has been a growing concern for children in developing economies, namely lack of access to quality customized online content in the classroom and at home; lack of tools that make learning fun and effective in key subject areas; and lack of state resources to meet educational demands. The Rumie Initiative was founded to tackle these concerns. It is a non-profit organization bringing educational content to the world's underprivileged children through low-cost technology. The Rumie Initiative's vision is to redefine the way education is provided, and through this, significantly reduce poverty and drive economic development. The primary advantage of The Rumie Initiative over all other educational technology solutions is that its content is specifically tailored to meet the local educational needs and curriculum standards. Utilizing the vast reserves of free online updated educational content available today; these android driven tablets are an affordable and intuitive way to deliver pre-loaded content without the requirement of internet access. Through mass global volunteerism, The Rumie Initiative collaborates with educators in choosing appropriate educational content and distributes content-loaded tablets through local Non-Governmental Organizations, communities and government entities. The Rumie Initiative has started a program of trials around the world and recently received a positive response at a Computer - Based Math Education Summit in New York hosted by UNICEF from November 21stto 22nd2013. The initiative was first launched in Haiti on 25thOctober 2013 and has since, been gaining momentum, currently exploring opportunities in Sub-Saharan countries such as Ghana, Kenya, Uganda, Rwanda, Burundi, South Africa and Nigeria. This paper focuses on The Rumie Initiative's unique business model to reach the masses, emphasizing issues relating to the power of volunteer ism, the forging of strategic development partners, and content-oriented technologies in increasing access to education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".