Ideological and Political Education of College Students in the New Media Environment—Taking Haiyuan College of Kunming Medical University as an Example
Bibliographic record
Abstract
Based on the current new media environment, new development possibilities and new challenges have emerged in the teaching methods, teaching contents, ideological and political value orientation and other modules of college students' ideological and political education. However, there are also many problems and weaknesses to be solved in the implementation and practice of the current ideological and political education for college students. Therefore, if colleges and universities want to overcome the drawbacks and problems of ideological and political education of college students under the current new media environment, they need to actively explore innovative ways of ideological and political education, improve the effective guarantee institutions for students' ideological and political education, and skillfully penetrate the elements of the development of the times, in order to strengthen the faculty of ideological and political education, enrich the methods of ideological and political education in combination with the new media environment, and cultivate students to establish correct ideological and political awareness. We should let the effect of college students' ideological and political education be further improved and enhanced in the new media environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".