Non-linear Analysis Method to Enhance the Strategy of Civic and Political Education
Bibliographic record
Abstract
Ideological and political education has the teaching characteristics of keeping pace with the times.In this paper, the nonlinear support vector machine is used as the ideological and political text data classification algorithm, combined with the text mining technology to collect and screen the ideological and political education data, and the ideological and political text data is divided into various clustering centers of ideological and political education, which are reflected in the research themes of ideological and political education, the hot spots of curriculum ideological and political research, and the teaching methods of ideological and political teachers.This paper analyzes the acquisition of ideological and political education resources from the perspective of students, and explores the matching degree between the acquisition of ideological and political education resources and the individual needs of students.The research objects and research hotspots of ideological and political education are divided, and the optimization strategy of ideological and political education is proposed.In the classification of research topics, the frequency of "college students" was the highest, which was 12568, and the calorific value of the research content "ideological and political education" and the research object "college students" was 8654, indicating that ideological and political education mainly revolved around "college students".The matching degree between ideological and political education resources and students' individual needs was 69.37%.Combined with the results of nonlinear analysis, ideological and political education can improve the effectiveness of educational content, strengthen the coupling degree between research content and research object, and strengthen the teaching factor of teachers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".