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Record W3208357075 · doi:10.23977/aetp.2021.55014

The Evolution of our Country's Civic Education Curriculum from the End of Qing Dynasty to the Beginning of the Republic of Our Country

2021· article· en· W3208357075 on OpenAlexvenueno aff
Ou Xie

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumChinaThe RepublicPoliticsPeriod (music)Political scienceRationalitySociologySocial scienceEconomic growthLawTheologyEconomics

Abstract

fetched live from OpenAlex

Throughout the late Qing Dynasty, our country has been affected and impacted by different historical stages in terms of politics, economy, and culture. In modern times, the citizens have been infiltrated by thoughts and formed a concept of Western learning spreading to the east. Therefore, our country's educational thoughts have also changed, and educational curricula have also been changed accordingly. This article aims to explore the reform and changes of our country's education curriculum from the end of Qing Dynasty to the beginning of the Republic of our country. Taking history as a mirror can promote the development of education in our country. This article mainly uses the questionnaire survey method and the data analysis method to read and understand the educational history of our country at the end of Qing Dynasty and the beginning of the Republic of China, and make a survey on the rationality of the civic education curriculum in the end of Qing Dynasty and the beginning of the Republic of China. The survey results show that the civic education in the late Qing Dynasty and the early Republic of China had a certain significance of the times, and it was a better way of education in the environment at that time. 40% of people are satisfied and agree with the civic education of that period.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.367
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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