Research on "Ideological and Political Education (IPE) in the Curriculum" Reform Practice—The Course: "Digital Marketing of Financial Products" as an Case Study
Bibliographic record
Abstract
This article focuses on the "Ideological and Political Education in the Curriculum"(IPE) reform practice of the course "Digital Marketing of Financial Products" and elaborates in detail how to deeply integrate IPE elements into the whole course teaching. Through the implementation of the overall course design plan, the paper explores the IPE - oriented education approach of "Course - Workplace Requirement - Qualification Certificate - IPE Elements" integration and realizes the organic unity of knowledge transmission and value guidance. The research adopts a variety of teaching methods and means, such as case, Q&A, and OBE approaches, and combines information resources to create a learning environment for students where they can "Acquire Knowledge and IPE Anytime and Anywhere". The article can have a positive impact on the professional training of teachers and students, scientific research, technical exchanges, and social services, which provides valuable experiences and examples for the IPE reform practices around the country.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".