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Record W4403847924 · doi:10.5539/jel.v13n6p317

Research on the Cultivation of Education for National Identity in High School Geography

2024· article· en· W4403847924 on OpenAlexvenueno aff
Jiqiang Niu, Yiyan Wang, Jiao Chen

Bibliographic record

VenueJournal of Education and Learning · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)SociologyMathematics educationPedagogyGeographyPsychology

Abstract

fetched live from OpenAlex

National identity is an important national conscience and plays a key spiritual role in maintaining national unity, stability and promoting the healthy and rapid development of the country. As an essential driver of education, the discipline must assume a major responsibility for national identity education. This study integrates national identity education into high school geography teaching, combines the latest geography teaching theory system with literature research method, text analysis method to analyze the components and specific content of national identity in high school geography teaching, and establishes a new teaching model. Simultaneously, the theme of “maritime rights and interests and ocean development strategy” was selected for teaching experiment, and the impact of the teaching mode proposed in this paper on the students’ learning efficiency in geography is investigated by questionnaire. The results of the teaching experiment show that the proposed teaching model can improve students’ awareness of national identity and serve as a reference for the teaching of national identity in subject subjects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.004
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.041
GPT teacher head0.408
Teacher spread0.367 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2024
Admission routes1
Has abstractyes

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