The Gender Digital Divide and Education in Afghanistan: A Review
Bibliographic record
Abstract
Amidst the strides the global society has made in digitalization, Afghanistan faces profound obstacles on its journey to embrace the digital era and foster a digital society, particularly for women. The current Afghan government, headed by the Taliban regime, effectively curtails women’s access to education, the Internet, and digital advancements. As underscored by the United Nations gender inequality index, Afghanistan ranks 157th among 162 countries in gender equality, reflecting its daunting challenges for women. The World Economic Forum also highlights a distressing gender digital divide within the nation. Faced with these disquieting realities, it becomes imperative to confront and narrow this divide in a collaborative endeavor. Acknowledging our shared responsibility, we can take substantial strides toward fostering gender parity and bolstering digital inclusivity in Afghanistan. This research seeks to pinpoint social and economic barriers and propose targeted solutions rooted in the most pressing needs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".