Research on the Intelligent Development Path of Civic Education Theory System in the New Era Based on Deep Learning
Bibliographic record
Abstract
This topic is based on the perspective of diagnostic evaluation, formative new evaluation, summative evaluation, in-depth analysis of deep learning to help the intelligent development of ideological and political education. The initially formulated questionnaire was modified several times, and the questionnaire design task was finally completed, and the formal distribution of questionnaires began to obtain the data for this study. At this level, the empirical research method combining quantitative research and qualitative research is used to deeply analyze the current situation of the intelligent development of ideological and political education in colleges and universities under the perspective of deep learning, with a view to contributing to the intelligent development of ideological and political education in colleges and universities. The mean value of the five dimensions of ideological and political education to stimulate the effectiveness of active learning (A1), promote the degree of in-depth understanding (A2), deepen the effect of interactive participation (A3), enhance the ability of higher-order thinking (A4), and expand the quality of the transfer of the use of the quality of the five dimensions (A5) is higher than 3.55 points, in addition to the significant difference in the characteristics of the samples (gender, academic qualifications, major, political profile), and puts forward five paths for the development of the five development paths, which are aimed at helping to promote the new era of the intelligent development of Civic and Political Education.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".