Discussion on the Construction Strategy of WeChat Public Platform Based on Ideological and Political Education in Colleges and Universities
Bibliographic record
Abstract
WeChat transmission has the characteristics of fast transmission speed, strong content selectivity and wide spread. The construction of WeChat public platform based on the ideological and political education in colleges and universities is a work that cannot be ignored in the ideological and political education, which is the supplement and extension of the practical teaching of ideological and political courses in colleges and universities, and can significantly enhance the pertinence and effectiveness of ideological and political education. The construction of WeChat public platform based on ideological and political education in colleges and universities should stick to the core idea of platform construction, define the stage target of the construction, build a professional team of WeChat public platform operation, improve the operation mechanism, integrate the resources of WeChat public platform construction, carefully consider the details of WeChat public platform operation, and collect feedback information and respond effectively, so as to improve the construction quality of WeChat public platform and enhance the transmission effect. In view of the deficiencies in the construction of WeChat public platform for ideological and political education in colleges and universities, measures should be taken to improve the communication power, influence, guidance and credibility of WeChat public platform, so that WeChat public platform can give full play to its value orientation and knowledge dissemination role in ideological and political education in colleges and universities.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".