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Record W4390230884 · doi:10.23977/aetp.2023.071719

Research on the Implementation Path of Ideological and Political Education in Civil Engineering and Architecture Major Courses from the Perspective of Critical Thinking

2023· article· en· W4390230884 on OpenAlexvenueno aff
Zhiyao Ma

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyPerspective (graphical)PoliticsVocational educationArchitectureCurriculumFunction (biology)Mathematics educationProcess (computing)Critical thinkingPedagogySociologyEngineering ethicsComputer sciencePsychologyPolitical scienceEngineeringLawArtificial intelligence

Abstract

fetched live from OpenAlex

"Curriculum thinking and politics" plays an important role in constructing the new pattern of ideological and political education in vocational colleges, and it is of great practical significance for the educational function of each course to give full play to the new pattern of education. At present, there are many problems in ideological and political teaching of civil architecture courses. Critical thinking can cultivate students' independent thinking ability, ability to distinguish information, critical awareness and language ability, as well as judgment and decision-making ability. Combined with the characteristics of civil and architectural majors, we explore the implementation path of teaching ideas, teaching objectives, teaching process, teaching methods, teaching resources, teaching evaluation and other aspects under the perspective of critical thinking.

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)0.018
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 score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.491
Teacher spread0.457 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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