The Researches on the Ideological and Political Education Models of College Students From the Perspective of Network Discourse
Bibliographic record
Abstract
The emergence, application and popularization of network discourse bring great challenges to the traditional model of ideological and political education. Network discourse bears the characteristics of material reality, content dominance and discursively by nature. These characteristics cause the changes of the college students’ ideological and political education model, from the subjectivity education model of intersubjectivity education model, from the uniform education content to diverse educational content. The model evolves from a closed and static situation to an open and dynamic education environment. Therefore, the contemporary scholars in the field of college students’ ideological and political education should constantly explore new models of the university students’ ideological and political education, appealing to the demand of the transformations in education conception, the openness of education approaches. They should apply the intersubjectivity education model and enrich the educational content. They should also attach importance to the diversity education model and construct an appealing educational atmosphere. Dynamic education is the direction so the new development and demand of the ideological and political education can adapt to requirements in the network + era.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".