Positive Psychological Intervention to Enhance Ideological and Political Education on the Internet in Colleges and Universities Practical Exploration of Effectiveness
Bibliographic record
Abstract
Network ideological and political education is the lifeline of the work of colleges and universities, and is a major political task and strategic project related to the cultivation of newcomers of the times, so the ideological and political education of colleges and universities should adhere to the principle of "advancing according to the times", and keep up with the times with continuous innovation and enrichment. [1]In the network ideological and political education of colleges and universities, by cultivating students' positive psychological qualities and stimulating their inner vitality and potential, and accepting network ideological and political education with a more open mind, we can achieve the goal of ideological and political education, and promote students' ideological and political level and all-round development. This study firstly investigated the current situation of network ideological and political education of 4374 students in a university in Xi'an, and then carried out stochastic intervention experiments for 480 students using the method of Positive Psychology Interventions. The results of the experiment showed that the positive psychological level and ideological and political level of college students were improved through positive psychological intervention. [2] On this basis, the model of positive psychological intervention to enhance the effectiveness of network ideological and political education is sorted out, which provides new ideas for the reform and innovation of the network ideological and political education model in colleges and universities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".