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Record W2882985730 · doi:10.15273/ijge.2018.03.008

Challenges and Opportunities of Higher Education for International Mining Engineers in China: Based on the Practice at Chongqing University

2018· article· en· W2882985730 on OpenAlexvenueno aff
Li Liu, Yong Li, Chunhong Ming, Gun Huang, Zhaolong Ge

Bibliographic record

VenueInternational Journal of Georesources and Environment · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
FundersDivision of Graduate Education
KeywordsChinaCurriculumQuality (philosophy)Engineering managementEngineering ethicsSustainable developmentEngineering educationEngineeringHigher educationPolitical scienceBusinessSociologyPedagogy

Abstract

fetched live from OpenAlex

Under the background of mineral industry transforms towards global and sustainable development as well as the establishment of innovative country in China, higher requirements for the mineral engineering education have been putting forward. Based on the research on the demands of mining engineers in the domestic and foreign, the mining engineering education objective, which includes the characteristics of international, innovative and interdisciplinary (referred as ‘3I’), was determined. To achieve the ‘3I’ education objective, the international outlook, the economics and management knowledge, as well as the practical ability for knowledge using were enhanced in the new curriculum. Substantially, a new education system includes three sub-education models was generated. Practical outcomes show that the education system is effective on improving the overall quality of students, especially the innovative ability. In the end, the flaws in learning and teaching in the current education system were discussed, including students’ concerns on the global level and understanding different cultures should be strengthened, as well as the teaching contents and teaching philosophy have to satisfy the changes and the demands of the industry development.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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