Research on the Integration of New Media Technology and Ideological and Political Education Teaching
Bibliographic record
Abstract
The concept and characteristics of new media are summarized and summarized, and the influence of Ideological and political education in Colleges and universities is briefly analyzed, and the connotation and development process of Ideological and political education in Colleges and universities are briefly written. From two angles of the subject and object of Ideological and political education in Colleges and universities, the application of new media in the ideological and political education of colleges and universities is combined with the data from the questionnaire survey, and the quantitative and qualitative two analytical methods are used to analyze the application of the new media in the ideological and political education of colleges and universities. The existing problems are studied from four aspects: the application degree of the new media, the authority of the main body of education, the increase of education difficulty and the existing problems of the object of education. Through the in-depth study of the causes of the problem, the solution of the theoretical knowledge of other disciplines is put forward by the combination of theory and practice as a principle. Countermeasures and creatively explore new ways of combining new media with ideological and political education.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".