Bibliographic record
Abstract
The ultimate purpose of the development of this essay is to inform Chinese policymakers on how they can better implement education-related policies to minimise spatial and gender disparities in, and multi-faceted barriers to, educational opportunities. This essay, firstly, highlights policies that address spatial and gender disparities in education and structural barriers to girls’ education with the support of the frameworks of Women in Development (WID) and Gender and Development (GAD) in rural Chinese contexts. Secondly, this essay outlines the problems of rural female underdevelopment in least educationally and financially developed Chinese regions based on critical analyses on relevant statistics and studies. Thirdly, in spite of the Central Government’s and non-governmental organisations’ (NGOs) endeavours to facilitating education development, this essay investigates and analyses how gender inequalities in education persist due to the unaddressed multi-faceted barriers to girls’ education. These multi-faceted barriers include social, cultural, economic and otherwise educational impediments faced by rural poor Chinese girls. Lastly, this essay suggests state and NGOs’ policy and intervention to address such structural barriers to education and enhance rural girls’ decision-making powers and educational opportunities in the long-term.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".