Bibliographic record
Abstract
Observing from the whole world, women studies in different countries have the similar background and goal. However, due to the differences between countries, women studies in different countries will definitely develop in its own way. In China, women studies have emerged and are undergoing exploration and coming into being. Offering curriculum related to women studies to university students is an important approach to facilitate the development of women studies in China. This article puts forward the following questions to be discussed when considering to offer women studies curriculum in higher education institutions such as: What goal is to be achieved whatever form the women studies curriculum is going to take? What to be taught? Is there any difference in teaching methods between women studies and traditional courses? Should the curriculum of women studies be discussed and treated as one subject just as history or sociology? How would the scholars and professors, especially the administrators in universities respond to the teaching of women studies? What kind of influence will the curriculum and teaching of women studies exert on the higher education, academic research and even the policies in China? It manages to integrate the thinking about these questions into the thinking clues of two basic issues in teaching of women studies: How to understand being indigenous? How to deal with indigenous teaching of women studies in Chinese universities?
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".