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Record W4321790478 · doi:10.5430/jct.v12n1p318

Establishment of Core Capacity and Capacity Index Based on an Outcome-Based Ideological and Political Education Major in Chinese Universities

2023· article· en· W4321790478 on OpenAlexvenueno aff
Jiafu Liu, Cao Huan, Pengfei Chen, Feifei Chen

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
FundersDepartment of Education of Guizhou ProvinceGuizhou Education University
KeywordsAnalytic hierarchy processIdeologyDelphi methodPoliticsCore competencyCompetence (human resources)DelphiRanking (information retrieval)Political sciencePolitical educationPublic relationsPsychologyManagementComputer scienceEngineeringEconomicsSocial psychologyOperations researchMathematicsStatisticsLaw

Abstract

fetched live from OpenAlex

With the development of the society, most countries have begun to improve the level of education and are advocating the implementation of the theory of Outcome Based Education, which is used in this research to explore the development of a curriculum of Ideological and Political Education Major.The purpose is to determine the professional core competence, expand the core competence indicators, and determine the relative weight of the core competence indicators. 4 core capacities and 20 core capacity indices of the Ideological and Political Education Major in Chinese universities were established using the Modified Delphi Method with two types of expert questionnaires. The Analytic Hierarchy Process was used to establish a hierarchical structure, and the relative weight and ranking of each capacity index was calculated to ensure consistency and stability using the YAAHP software. The aim of the research is to further clarify the core capacity and capacity indices of education.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.297
Teacher spread0.275 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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