Practice on Practical Teaching Methods for Panoramic Ideological and Political Courses
Bibliographic record
Abstract
The ideological and political course is playing an important responsibility in cultivating higher ideological level and higher moral quality constructors for socialist cause, only under the helps and guiding of ideological and political educational courses, the learners can lead to the Socialism road with Chinese characteristics under the leadership of the Party consistently. The thoroughly pervasive globalization tendency and the prosperity and the development of the diversified culture have made significant changes to our country’s thought environment, while on the other hand, the ideological and political courses in our country haven’t conducted corresponding adjustment to this tendency, they are still featuring of numerous theoretic contents, severe dogmatism in teaching forms and other problems are prominent gradually, with the addition of the influence of complex thought environment from home and abroad, domestic learners haven’t paid enough attention to the importance of the ideological and political courses, and the course’s educational effect haven’t been exerted fully enough. And this following paper will propose a kind of panoramic ideological and political course practical teaching method focusing on the above issues and problems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".