"Internet +" Path to Improve the Quality of Ideological and Political Theory Courses in Higher Vocational Colleges
Bibliographic record
Abstract
The improvement of the quality of ideological and political theory courses in higher vocational colleges under the background of "Internet +" is one of the important ways to achieve the goal of high-quality talents training. The ideological and political theory course (hereinafter referred to as the ideological and political course) in higher vocational colleges is the main channel and main position for the systematic ideological and political education of higher vocational students. Through the investigation of 27 enterprises and 7 colleges, the research team found the following reform directions and countermeasures for the ideological and political course: 1. Adopt three kinds of information methods to mobilize students' interest in learning. 2. Strengthen the training in secondary school. 3. Explore new teaching models. 4. Compiling the ideological and moral cultivation and legal foundation course guidance as a supporting material to improve the effectiveness. 5. Adopt three practical teaching methods. 6. Make full use of the network to obtain course-related materials. 7. Divide the tasks in the form of modules to further improve the curriculum construction materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".