Socialization Path Innovation of Moral Education of College Students in New Media Era
Bibliographic record
Abstract
The socialization of moral education is an important goal of college personnel training in terms of moral education. With the development of information technology, new media are becoming increasingly popular and they are deeply integrated with college moral education socialization, having internally changed and influenced the shaping of personality of moral education of young students. The new media are mobile ubiquitous, anonymous and interactive, virtual reality and other media features, and they effectively promote the emotional cultivation of young students’ moral education, the waking of the consciousness of moral education, the shaping of value of moral education and the practice of moral education; however, the double-edged feature of technology brings worries and risks. The alienation of media information, the collective irrationality in the virtual space, group polarization, network populist phenomenon objectively reflect and threaten the socialization process of moral education of young college students. Based on this, we need to respond effectively, take measures to avoid the risk of moral socialization that new media has brought to the moral education of college students, and promote the socialization process of moral education of college students from four aspects: knowledge construction, platform building, cultural management and position construction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".