Bibliographic record
Abstract
Malala Yousafzai grew to global prominence in October 2012 when she was shot in the head by Taliban soldiers in Northwestern Pakistan while on her way home from school. Malala was shot due to her unwavering activism for women and girls’ rights to an education. The global war on terror was carried out in neighboring Afghanistan against the same enemy. The Taliban was largely mobilized by Western discourses of saving and freeing Afghan women. Much of this discourse was centered around the right of Afghan women and girls to attend school. What is often overlooked in Malala’s story, as well as the story of other Pashtun women in Southern Afghanistan and Northern Pakistan, is that Western military imperialism—worsened gender divisions and gender oppression. The Taliban is a by-product of U.S. covert operations in Afghanistan during the Soviet invasion and the Durand Line, the fictitious border annexing the Pashtun tribes imposed by British colonial rule. Both occupations have had devastating implications on womens’ and girls’ access to education, healthcare, and resources. Malala’s story is powerful and inspirational; however, it is also one that has been appropriated by the West, one that reinforces a narrative of Muslim women’s oppression and helplessness, and one that forgets the years of resistance and activism by women and girls in the region. I write this chapter as a Pashtun woman, and member of the Yousafzai tribe, a granddaughter of the Swat Valley, split up from tribal brothers, sisters, and cousins by Western imperialism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".