English-medium education and the perpetuation of girls’ disadvantage
Bibliographic record
Abstract
In our community, girls do not need this [English-medium education]. Interview with male teacher Nepal is classified as a low-middle income country (World Bank, 2023), and like other such countries, it is under international pressure to attain gender equality targets in order to receive international aid. However, Nepal is also permeated by widespread perceptions that girls are subordinate to boys, which influences girls’ access to education, information, health and the labour market (Upadhaya & Sah, 2019). Women face restrictions in terms of their basic ability to ‘independently venture outside the household, maintain the privacy of their bank accounts, use mobile phones, or become employed’ (Karki & Mix, 2022: 413). Illiteracy disproportionately affects females, with 58.95% of illiterates being women and girls (UNESCO, 2021). Notwithstanding this, recent years have seen some progress in enhancing gender equality in Nepal, and females currently enjoy higher enrolment rates than males across secondary education (UNESCO, 2023). This article, however, provides evidence that the recent trend to offer English-medium education risks setting back progress made by creating a gender-differentiated system that could yield different outcomes for boys and girls and potentially restrict girls’ future trajectories post school and contribute to broader gender inequality in society.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".