Bibliographic record
Abstract
Illiteracy in the Arab world is becoming an urgent necessity particularly facing problems of poverty, ignorance, extremism, which impede the required economic, social, political and cultural development processes. Extremism, violence and terrorism, in the Arab world, can only be eliminated by spreading of knowledge, fighting illiteracy. The study shows that illiteracy rate among males in the Arab world is 25% for males, (46%) for Females. Results of the study show that if the educational situation in all Arab countries does not change, illiteracy rates will increase in the Arab world, and the number of illiterates in the Arab world will reach 49 million in the category of age of 15 years, and by 2024,it may reach 5.5 million of youth (15 - 24 years). The study identifies factors affecting the rise of illiteracy in the Arab world, particularly: Low economic level of many Arab countries, the growing security, political turmoil and internal problems experienced by most Arab countries, Social reasons, and random policies and contradiction in the trends and areas of combating illiteracy. The study concluded that illiteracy has a significant impact on social behavior, and that democracy, political participation, violence, cultural development, respect, pluralism, and accepting diversity, are all affected by illiteracy. The study recommends that Arab governments must formulate clear strategies linked to development plans to save 100 million Arab citizens who suffer from illiteracy, and ignorance. Illiteracy is to be taken seriously because it entails misunderstanding democracy, lack of youth interest in political affairs, corruption, and therefore the absence of comprehensive reform programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".