Supply-side Reform of University Ideological and Political Education Based on Big Data
Bibliographic record
Abstract
With the development of science and technology, people use more and more data, so big data plays a key role in the problem of excessive capacity. This article mainly introduces the collaborative innovation research on the supply-side reform of ideological and political education (IPE) of university campus culture based on big data, and intends to provide some ideas and directions for the collaborative innovation research on the supply-side reform of university IPE. This paper proposes a collaborative innovation research method based on big data for the supply-side reform of IPE on university campus culture, including document retrieval method, interview method, questionnaire survey method, multidisciplinary research methods and big data-based research methods. The experimental results of this article show that the average value of the correlation coefficient α of the questionnaire reliability is 0.91, indicating that the reliability of the questionnaire in this article is relatively high, and it can provide relevant references for this research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".