On the Teaching Interactive Paradigm of Network Training for Civil Servants in the New Era: Based on the Perspective of Post-modern “Teaching by proposal” Theory
Bibliographic record
Abstract
With the rapid development of modern educational technology, the civil servants’ training paradigm in China has quietly moved from the model of traditional single offline (face-to-face) to a hybrid training paradigm that combines online and offline. However, compared with the attention paid to the expensive construction of network training platform and network training curriculum resources, the current network training of civil servants in our country has always lacked high attention and effective methods to a series of problems affecting the teaching quality and effect in network training, such as the reform and innovation of teaching paradigm, the stimulation of students’ learning motivation, the in-depth integration of teaching and technology, the cultivation of students’ autonomous learning ability and thinking mode. The fundamental reason is that there is a problem with the interactive teaching paradigm of civil servants’ network training. Therefore, how to effectively reform and innovate the interactive paradigm of network training and teaching for civil servants in our country has become a key problem that restricts the high-quality development of network training for civil servants in the new era. The post-modern “teaching by proposal” theory put forward by John B.Cobb Jr., a famous American post-modern philosopher, is a brand-new interactive teaching theory, which has very important enlightenment significance and application value for the construction of the interactive teaching paradigm of China’s civil servants’ network training. Article explains postmodern “ teaching by proposal” theory of rich connotation and methodological essence based on point,Starting from the main problems and key causes of teaching interactive paradigm of civil servants network training ,guided by postmodern” teaching by proposal” interactive theory, construct a kind of teaching interactive paradigm of civil servants network training, civil servants’ autonomous learning ability as the purpose, to “teaching proposition” precise “further proposed as the main method, “ interesting proposition “, “useful proposition”, “effective proposition” cycle as the main part.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".