Bibliographic record
Abstract
In this paper, a scientometric analysis of 382 academic publications on civic education published from 2017 to 2022 is presented to characterise the curriculum education by identifying and visualising the evolution of the collaboration network, the institution and the emerging trends according to the development of civic education.The results indicate that the number of publications in recent years surged, and cover aspects like course study, history research and learning method in different grades.Besides the building of curriculum, the articles also focus on the implementation of curriculum.The most productive network authors are from the USA, China, UK and Canada.However, the in-depth analysis, evaluative involvement and empirical study is seldom mentioned.The paper presents an in-depth analysis of civic education in curriculum to better educate the citizens in the new era.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.042 | 0.060 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".